Public Opinion of Gene-Editing in Agriculture: A Mixed-Method Study of Online Media and Metaphors
by
Nellie Hill, M.S.
A Dissertation
In
Agricultural Communications & Education
Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of
DOCTOR OF PHILOSOPY
Approved
Dr. Courtney Meyers Chair of Committee
Dr. Nan Li
Dr. David Doerfert
Dr. Venugopal Mendu
Mark Sheridan Dean of the Graduate School
August, 2020
Copyright 2020, Nellie Hill Texas Tech University, Nellie Hill, August 2020
ACKNOWLEDGEMENTS
Thank you to the faculty of the Department of Agricultural Education and
Communications at Texas Tech. The guidance and encouragement of faculty in my learning, researching, and teaching was extremely valuable in my doctoral program as well as career ahead. I am especially grateful to my chair, Dr. Courtney Meyers, for her years of support and mentorship. Additionally, I am thankful for my committee members
Dr. Nan Li, Dr. David Doerfert, and Dr. Venugopal Mendu for their investment of time, energy and attention to the development and execution of my qualifying exams and dissertation project.
Thank you to Dr. Lindsay Kennedy who made sure we were always reaching towards exciting goals in arenas in and out of academia, and having fun doing so. I am grateful we are life-long colleagues, teammates, and most importantly, friends.
Thank you to Katelin Spradley, Taylor Belle Matheny and Whitney Whitaker. Without these three colleagues who unquestionably contributed their time and coding skills, this dissertation would not be possible.
Thank you to Katelin Spradley and Kim Cantrell for their passionate dedication to the role of teaching assistant in my undergraduate classes. The students and I are indebted to you for your attention to detail and positivity in handling any issue that arose.
Thank you to Dr. Barry Flinchbaugh, who first inspired me to become a professor.
Thank you to all of the teachers, mentors, and students who inspired and motivated me to accomplish this long-held goal.
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This material is based upon work that is supported by the National Institute of
Food and Agriculture, U.S. Department of Agriculture, under award number 2017-70001-
25991.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS ...... ii ABSTRACT ...... vii LIST OF TABLES ...... ix LIST OF FIGURES ...... x I: INTRODUCTION ...... 1 Background and Setting ...... 1 Brief History of Gene-Editing ...... 1 Role of Gene-Editing in Agriculture...... 3 Public Opinion of Gene-Editing in Agriculture ...... 5 Need for the Study ...... 7 Purpose and Research Questions ...... 8 Frameworks ...... 9 Diffusion of Innovation Theory ...... 9 Conceptual Metaphor Theory ...... 10 Framing ...... 12 Elaboration Likelihood Model ...... 13 Structure-Mapping Theory...... 14 Research Design and Rationale ...... 15 Scope of Investigation, Assumptions, and Limitations ...... 17 II: A DESCRIPTIVE ANALYSIS OF TWITTER CONTENT REGARDING THE USE OF GENE-EDITING IN AGRICULTURE ...... 19 Abstract ...... 19 Introduction ...... 20 Literature Review ...... 23 Twitter Characteristics and Uses ...... 23 Social Media Monitoring and Gene-Editing on Twitter ...... 24 Conceptual Framework ...... 26 Purpose and Research Questions ...... 29 Methodology ...... 29 Sampling ...... 30 Data Analysis ...... 31 Results ...... 32 Discussion, Conclusion & Recommendations...... 40 References ...... 49
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III: A SYSTEMATIC METAPHOR ANALYSIS OF GENE-EDITING IN AGRICULTURE IN ONLINE UNITED STATES NEWS ...... 54 Abstract ...... 54 Introduction ...... 55 Influence of News Media Coverage of Science ...... 56 Systematic Metaphor Analysis ...... 58 Literature Review ...... 60 Media Coverage of Biotechnology ...... 60 Media’s Use of Metaphors ...... 62 Conceptual Framework ...... 63 Framing ...... 64 Conceptual Metaphor Theory ...... 65 Purpose and Research Questions ...... 67 Methodology ...... 68 Selection of Units ...... 68 Unit Analysis ...... 71 Dependability and Credibility ...... 73 Results ...... 75 Discussion, Conclusions & Recommendations ...... 81 References ...... 88 IV: PERSUASIVE EFFECTS OF METAPHORS REGARDING GENE-EDITING IN AGRICULTURE ...... 96 Abstract ...... 96 Introduction ...... 97 Literature Review ...... 102 Public Opinion of Gene-Editing ...... 102 Metaphors for Gene-Editing in the Media ...... 104 Conceptual Framework ...... 107 Elaboration Likelihood Model ...... 107 Structure-Mapping Theory...... 110 Purpose and Research Questions ...... 112 Methodology ...... 112 Instrumentation ...... 113 Manipulation Check ...... 117 Participants ...... 122 Procedure ...... 123 Data Analysis ...... 124 Results ...... 131 v Texas Tech University, Nellie Hill, August 2020
Discussion, Conclusions & Recommendations ...... 136 References ...... 139 V: IMPLICATIONS & RECOMMENDATIONS ...... 148 Overview of Study Phases ...... 149 Phase One...... 149 Phase Two ...... 149 Phase Three ...... 150 Implications ...... 151 Phase One...... 151 Phase Two ...... 152 Phase Three ...... 153 Summary ...... 154 Recommendations ...... 155 Research ...... 155 Practice ...... 157 References ...... 159 APPENDIX A: SURVEY INSTRUMENT...... 167 APPENDIX B: IRB APPROVAL...... 190
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ABSTRACT As products of gene-editing technology get closer to retail shelves, it is important for science communicators to disseminate information regarding the applications and implications of the technology. Public acceptance of gene-editing in agriculture will be influential to overcoming the challenges of a growing world population and a changing environment. The overarching purpose of this study was to understand how the United
States’ public and news media discuss gene-editing applications in agriculture and what impact the context in which the topic is discussed has on public opinion. To accomplish this purpose, three independent, yet interconnected, research phases were conducted to describe the public discussion, examine the news media discussion, and test the influence of metaphors on public acceptance of gene-editing in agriculture.
During phase one, a Meltwater social media monitor collected N = 13,189 relevant tweets for analysis. Between September 1, 2018 and December 31, 2019, the amount of conversation regarding gene-editing in agriculture, the number of contributing
Twitter accounts, and the reach of the conversation was relatively stable. In contrast, engagement with the conversation was on the rise and the sentiment of tweets was became increasingly positive. Public accounts with the most reach were predominantly news organizations, while accounts with the greatest engagement were a mix of news accounts and individual accounts.
During phase two, the Nexis Uni database was utilized to collect 26 U.S. news articles concerning gene-editing in agriculture published online by four national news media between 2015 and 2019. These articles facilitated a qualitative systematic metaphor analysis; that is, they were examined to identify the metaphors used to describe
vii Texas Tech University, Nellie Hill, August 2020 gene-editing in agriculture in terms of process or product. The metaphors were then analyzed to develop a list of underlying metaphorical concepts and their frequency of occurrence. The articles conceptualized the topic as creation, a coding program, a fighter, math, targeting, a text editor, and a tool. Overall, the concepts address the complexity, detail, and skill required by gene-editing technologies in agriculture.
During the final phase of the study, a between-subjects, experimental survey research design was used to investigate which metaphorical concept for gene-editing in agriculture causes the most issue-relevant thinking and willingness to share on social media. The metaphors of gene-editing as creation, text editor and tool were embedded into mock news articles. A control mock news article was also created. Three hundred participants provided demographic information, indicated their deference to scientific authority, and responded to items regarding their perceived and factual knowledge of gene-editing in agriculture. After reading the randomly assigned mock news article, participants shared their thoughts and indicated their willingness to subsequently share the article on social media. Even when controlling for confounding variables, the results indicated no significant differences between the treatments on issue-relevant thinking or willingness to share the article on social media.
The results of this study give researchers, science communicators and agricultural communicators a more complete understanding of the current conversation around gene- editing in agriculture in terms of who is participating, what is being said, and how it can be explained to the public. This study indicated there is steadily increasing interest in the topic of gene-editing in agriculture, yet the most effective way of explaining the complex science of the topic remains unclear.
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LIST OF TABLES
2.1 Gene-editing in Agriculture Top 10 Twitter Accounts with Regards to Reach ...... 34
2.2 Gene-editing in Agriculture Top 10 Twitter Posts with Regards to Engagement...... 36
2.3 Highest Reach and Engagement Tweets by Month Regarding Gene-editing in Agriculture ...... 43
3.1 Metaphorical Concepts Describing Gene-Editing Applications in Agriculture ...... 75
4.1 Analysis of Covariance of Elaboration Regarding Gene-Editing in Agriculture, with Individual Difference Variables as Covariates ...... 133
4.2 Analysis of Covariance of Willingness to Share on Social Media Information Regarding Gene-Editing in Agriculture, with Individual Difference Variables and Elaboration as Covariates ...... 135
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LIST OF FIGURES
2.1 Frequency of Gene-editing in Agriculture Tweets and Unique Twitter Users Contributing ...... 32
2.2 Reach of Gene-editing in Agriculture Tweets ...... 33
2.3 Frequency and Engagement with Gene-editing in Agriculture Tweets ...... 35
2.4 Sentiment of Gene-editing in Agriculture Tweets ...... 38
3.1 Prominence of Each Gene-Editing Metaphorical Concept ...... 80
4.1 Mock News Article for Control ...... 118
4.2 Mock News Article for Metaphorical Concept of Creation ...... 119
4.3 Mock News Article for Metaphorical Concept of Text Editor ...... 120
4.4 Mock News Article for Metaphorical Concept of Tool ...... 121
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CHAPTER I
INTRODUCTION
This chapter will describe the history of gene-editing as a whole, delve into the applications of gene-editing in agriculture, and provide a glimpse into the public opinion of the topic. The importance of effectively communicating gene-editing applications in agriculture to the public is explored. The study’s need, purpose, research questions, frameworks, rationale and scope are also detailed for each of three phases. The three phases are utilized to more fully research the overarching purpose of the study, which is to understand how the United States’ public and news media discuss gene-editing applications in agriculture and what impact the context in which the topic is discussed has on public opinion.
Background and Setting
Brief History of Gene-Editing
Modern gene-editing technology has the power to fundamentally change the chemistry of humans, plants, and animals (Molteni, 2019). The ability to change the
DNA bases that make up the genomes of eukaryotic organisms gives rise to incredible opportunities in molecular biology, medicine, and biotechnology (Adli, 2019). The recent explosion of scientific advancement in the field of genetic engineering has a laborious lineage dating back to the early 1980s. Five technologies have been used for targeted genome editing (i.e. gene-editing) since then: Oligonucleotide Directed Mutagenesis
(ODM), Zinc-Finger Nucleases, Meganucleases, Transcription Activator Like Effector
Nucleases (TALENs) and Clustered Regularly Interspaced Short Palindromic Repeats
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(CRISPR)-Systems. Modern gene-editing mainly uses TALENS and CRISPR-systems, which can be used with and without DNA (Metje-Sprink et al., 2019).
Clear definitions and boundaries for the different types of gene technology are difficult to come by in the literature (Huang et al., 2016; Johnson, 2015; Tagliabue,
2015). There is a lack of clarity in the terminology referring to categories of technological methods used to change a living thing’s genetic information through targeted addition, removal, or alteration of genetic material in a manner that would not naturally occur. Broadly, Hiu et al. (2016) described genome engineering as the “process of making targeted modifications to the genome, its contexts (e.g., epigenetic marks), or its outputs (e.g., transcripts)” (p. 2). From here, the way such modifications are applied to
DNA parses out into two different categories – genome modification or genome editing.
However, genome engineering, modification, and editing have all been criticized in academia and the mass media for being difficult to clearly and definitively define (Huang et al., 2016; Johnson, 2015; Tagliabue, 2015). The National Institutes of Health (2020) clarify for gene-editing in the U.S. to mean a group of technologies that give scientists the ability to change a plant or animal’s DNA. These technologies allow genetic material to be added, removed, or altered at particular locations in the genome without the introduction of foreign genes into the organism (National Institutes of Health, 2020).
One term news media outlets use to describe the boom in DNA-free CRISPR technology is gene-editing. This biotechnology has the relatively simple and inexpensive ability to remove, add, or alter precise, user-selected sections of DNA (Doudna &
Charpentier, 2014; Rajendran et al., 2015). The relative ease of use has resulted in headlines featuring super babies, extinct species saviors, and malaria-eradicating
2 Texas Tech University, Nellie Hill, August 2020 mosquitoes that have grabbed the attention of the public, even as they cannot grab the products of CRISPR gene-editing off retail shelves. Nevertheless, gene-edited groceries are in progress, including staple ingredients to many food products such as a variety of soybeans that are drought-tolerant and another variety that produces a healthier oil
(Molteni, 2019). Gene-editing applications will continue to increase across plant and animal kingdoms as scientists discover new systems and evolve the field (Adli, 2018).
Role of Gene-Editing in Agriculture
The agricultural industry has a storied history of adopting innovations early in the development of new technologies and this continues with the adoption of gene-editing technology (Goold et al., 2018). Gene-editing has been heralded as a critical innovation to meet the needs of a growing world population also dealing with changing climate conditions (Shew et al., 2018). There are many applications for gene-editing in plants and animals, including disease control, pest eradication, speed breeding, nutritional and quality enhancements, extreme weather resistance, and deletion of allergies (Doudna &
Charpentier, 2014; Duran, 2016; Goold et al., 2018; Rajendran et al., 2015; Regalado,
2015; Shrock & Güell, 2017).
Gene-editing has been used in 14 plant species to date, including soybeans, wheat and rice (Metje-Sprink et al., 2019). The technology is not currently applicable to all species mostly due to the lack of usable in vitro techniques. Gene-editing, specifically
CRISPR-Cas9, has been applied to animal species including cattle, pigs, monkeys, mice, salmon, fruit flies, and worms (Gallegos, 2020; Shrock & Güell, 2017). In general, gene- editing is used for making specific improvement of desired traits in commercial plants and animals. This technology is the most advantageous to do this work because of its
3 Texas Tech University, Nellie Hill, August 2020 superior speed, precision, and cost (Metje-Sprink et al., 2019). These advantages have spurred considerable exploration of how the technology can be utilized in developing food products that will be integrating into the food system at the retail level (Metje-
Sprink et al., 2019; Molteni, 2019).
Gene technology laws govern the labeling and sale of foods produced used genome engineering methods in the United States and abroad. As such, DNA-free genome engineering emerged in the mid-2000s as a means to possibly circumvent these laws for the benefit of the food supply (Metje-Sprink et al., 2019). The advantages of
DNA-free gene-editing systems include incredibly precise and accurate gene cutting, and inherent elimination of foreign DNA being integrated into the genome (Metje-Sprink et al., 2019). While genetic modification and genome engineering may be commonly equated in literature, federal regulations stipulate the two are different.
In the United States, after much public debate, the U.S. Department of Agriculture will require labeling of bioengineered foods beginning on January 1, 2022. U.S. Secretary of Agriculture, Sonny Perdue, explained the purpose of the labeling, per the National
Bioengineered Food Disclosure Standard, was that it “increases the transparency of our nation’s food system, establishing guidelines for regulated entities on when and how to disclose bioengineered ingredients. This ensures clear information and labeling consistency for consumers about the ingredients in their food” (USDA Press, 2018, para.
2). Bioengineered foods are defined as “those that contain detectable genetic material that has been modified through certain lab techniques and cannot be created through conventional breeding or found in nature” (United States Department of Agriculture, n.d., para. 2). The key phrase in the USDA’s definition is foods must contain detectable
4 Texas Tech University, Nellie Hill, August 2020 genetic material. DNA-free gene-editing technologies leave no detectable genetic material (Metje-Sprink et al., 2019). Therefore, DNA-free gene-editing will be the focus of this study and referred to simply as gene-editing.
Public Opinion of Gene-Editing in Agriculture
The vast applications and recent advancements through the use of CRISPR have sparked debate regarding the social, cultural, and ethical implications of gene-editing technology (Brossard, 2018). While the public is forming their opinion about the use of gene-editing technologies in plants and animals, it is known that the public does not understand the science of gene-editing (Miller, 2004; Rainie, 2017). In addition, consumers may not distinguish the differences between genetically modified food and food enhanced by gene-editing (Ishii and Araki, 2016; Lusk et al., 2018; McFadden &
Lusk, 2016; Zerbe, 2004). Although there is great promise in applying this technology to improve agricultural production, there is great concern that the negative beliefs some consumers hold about genetically modified foods will carry over to agricultural applications of gene-editing (Rose et al., 2020; Huang et al., 2016; Ishii & Araki, 2016;
Molenti, 2019; Shew et al., 2018).
Negative public opinion of gene-editing applications has the potential to hamper further scientific advancement of gene-editing in plants and animals before point-of-sale is even a reality (Huang et al., 2016; Ishii & Araki, 2016; Shew et al., 2018). Public opinion is also known to be an influential consideration to politicians in positions to shape scientific policy (Burnstein, 2003; Frewer, 1999; Miller, 2004). Shew et al. (2018) provided hope in the results of their multi-country survey, which indicated respondents were more willing to consume products of CRISPR technology than genetic modification
5 Texas Tech University, Nellie Hill, August 2020 technology. The researchers suggested increasing consumer knowledge of biotechnologies to increase acceptance of gene-editing products (Shew et al. 2018).
However, consumers in the United States are already knowledgeable about science, with
39% of respondents to a Pew Research Center study classified as having high science knowledge and 32% classified as having medium science knowledge. In fact, of all respondents, 56% correctly identified inserting a gene into a plant as an example of genetic engineering (Kennedy & Hefferon, 2019). Studies have shown time and again that simply increasing public knowledge does not solve the science communication paradox (Akin & Scheufele, 2017; Brossard & Shanahan, 2007; Kahan, 2015; Nisbet &
Scheufele, 2007; Suldovsky, 2016).
The science communication paradox, as described by Kahan (2015), is persistent, divisive conflict in the face of compelling, scientific evidence due to facts being in opposition of identity, and identity winning out. It is important to note such conflict is not as pervasive as we might think. There is a larger number of non-polarizing science issues than polarizing issues. What is known about science is eventually agreed upon by communities (Kahan, 2015). Until then, the dominant explanation of why the paradox is still present is known as the Cultural Cognition Thesis. When positions on facts become associated with social groups, individuals will selectively assess evidence in ways reflective of their own group (Kahan, 2010). As Kahan (2010) explained, “The same groups who disagree on 'cultural issues' — abortion, same-sex marriage and school prayer — also disagree on whether climate change is real and on whether underground disposal of nuclear waste is safe” (para. 2). While there is no silver bullet to the complex,
“wicked problems” (Scheufele et al., 2017, p. 463) of divisiveness over genetic
6 Texas Tech University, Nellie Hill, August 2020 engineering, science communication researchers have proposed several different avenues to encourage community convergence on the topic. That is, assisting most people, but likely not all, in coming together to agree on how genetic engineering should be utilized and regulated. The avenues for convergence include means of framing (Druckman &
Lupia, 2017), reducing selective exposure and judgement (Stroud, 2017), reducing use of heuristics (Peters, 2017), and reducing fear of the unnatural (Lull & Scheufele, 2017).
Discussions around the wicked problems of science often happen via online communications, including reading online news articles and social media posts. As access to the internet and consumer preference to use it as a source of information continue to increase, so too does information seeking online (Brossard & Shanahan,
2017). A Pew Research Center study found the most common sources Americans receive news is television, followed by news websites, radio, social media, and finally print newspapers (Shearer, 2018). More than half of Americans, 68% according to another report from the Pew Research Center, do not seek out science news, but rather just happen across it on the internet (Funk et al., 2017). Social media platforms play an important role as 33% of Americans consider it an important way to get science news
(Funk et al., 2017). Media coverage of science news connects the scientific community and the public (Schäfer, 2012).
Need for the Study
As products of gene-editing technology get closer to retail shelves, it is important for science communicators to strategically disseminate the implications of the technology
(Brossard, 2018). Here, the agricultural industry has an opportunity to stave off stigmas, misrepresentations, and uproar in favor of public appreciation for the complexities of the
7 Texas Tech University, Nellie Hill, August 2020 food system (Charleston|Orwig, 2017; Genetic Literacy Project, 2016). The advancements in gene-editing technologies today will ripple through future generations, making it all the more important for science communicators to understand more about how to effectively communicate the products of this technology, such as those gene- edited soybean varieties (Molteni, 2019). Public acceptance of gene-editing is critical
(Travis, 2015). There is a need to investigate how the context in which gene-editing is discussed influences consumer public opinion so scientists and science communicators can effectively engage in conversation with the public with accurate and ethically-sound explanations of gene-editing in agriculture (Jamieson et al., 2017; O’Keefe et al., 2015;
Ruth et al., 2020; Shew et al., 2018).
Purpose and Research Questions
The overarching purpose of this study was to understand how the United States’ public and news media discuss gene-editing applications in agriculture, and what impact the context in which the topic is discussed has on public opinion.
A Descriptive Analysis of Twitter Content Regarding the Use of Gene-Editing in
Agriculture (Phase One)
RQ1: How many mentions of gene-editing applications in agriculture were
publicly posted on Twitter between September 1, 2018 and December 31, 2019?
RQ2: What was the social reach and engagement of those tweets?
RQ3: What was the sentiment of those tweets?
RQ4: How does tweet reach and engagement vary based on tweet sentiment?
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A Systematic Metaphor Analysis of Gene-Editing in Agriculture in Online U.S.
News (Phase Two)
RQ1: What metaphorical concepts do online U.S. news articles use to frame gene
editing applications in agriculture?
RQ2: What are the most prominent metaphorical concepts used in those articles?
Persuasive Effects of Metaphors Regarding Gene-Editing in Agriculture (Phase
Three)
RQ1: How does the metaphorical concept used to explain gene-editing
applications in an agricultural context influence elaboration?
RQ2: How does the metaphorical concept used to explain gene-editing
applications in an agricultural context influence willingness to share the
information on social media?
Frameworks
This multi-method study was guided by multiple frameworks. Diffusion of innovation theory (Rogers, 2003) was the theoretical lens for the descriptive analysis of
Twitter content. Conceptual Metaphor Theory (Lakoff & Johnson, 1980) and framing
(Goffman, 1974) and were utilized for the systematic metaphor analysis of national U.S. newspapers. The Elaboration Likelihood Model (Petty & Cacioppo, 1986) and the structure-mapping theory of metaphor (Gentner, 1982) guided the experimental phase of the study.
Diffusion of Innovation Theory
Dissemination of ideas, practices, and objects perceived as new by members of a social system, an innovation, requires two-way communication of the innovation through
9 Texas Tech University, Nellie Hill, August 2020 channels over time (Rogers, 2003). This diffusion of innovation process is an exchange of information between two or more individuals who will either converge or diverge on the innovation to create social change. Diffusion communication is unique in the uncertainty associated with innovation. Uncertainty is taken into account by the individual during their decision-making process about the innovation. The innovation- decision process is characterized by the following stages: (a) knowledge, (b) persuasion,
(c) decision, (d) implementation, and (e) confirmation (Rogers, 2003).
Communication channels, whether mass media or interpersonal, cosmopolite or localite, play a vital role in the innovation-decision process during the knowledge and persuasion stages. The time it takes for an individual to complete the process contributes to the overall rate of adoption of the innovation by members of the social system (Rogers,
2003). The innovation’s relative advantage, compatibility, complexity, trialability, and observability explain more than half of the variance associated with its rate of adoption
(Rogers, 2003). Goel et al. (2016) and Meng et al. (2018) found broadcast and viral communication patterns on Twitter to contribute to innovation adoption. Social media sharing behaviors has been found to indicate the sender’s approval or acceptance of the information (Kee et al., 2016).
Conceptual Metaphor Theory
Lakoff (1998) defined metaphor as “cross-domain mapping in the conceptual system” and a metaphorical expression as “a linguistic expression (a word, phrase or sentence) that is the surface realization of such a cross-domain mapping” (p. 202). Lakoff and Johnson’s (1980) Conceptual Metaphor Theory described human knowledge as a result of experiences, which are tied together to create meaning. This makes human
10 Texas Tech University, Nellie Hill, August 2020 cognition itself metaphorical (Lakoff & Johnson, 1980). In addition, Lakoff (1993) argued a significant amount of subject matter, especially topics within science, requires the use of metaphors to be understood.
The human perceptual system struggles with interpreting macrocosmic and microcosmic phenomena, such as solar systems and molecules. Therefore, scientists use metaphors to communicate so as to assist their audience with mapping meaning from everyday experiences to scientific complexities in order to make sense of science
(Niebert & Gropengiesser, 2015). Niebert and Gropengiesser (2015) provide several examples:
Robert Hooke was the first to denote the cell using the term “cell” when an image of a piece of cork under his microscope reminded him of the small rooms, or cells, occupied by monks in monasteries. Kepler developed his concept of planetary motion by comparison with a clock. Huygens used water waves to theorize that light is wavelike. Arrhenius described the greenhouse effect by referring to his experience with hot pots (p. 2).
Humans like to use metaphors because they make tying together experiences easier (Lakoff & Johnson, 1980). Previous research has shown metaphors used to describe gene-editing fail to capture the complexity of the technology with an accurate description of what it is and how it works as well as what is still unknown (O’Keefe et al., 2015; Pigliucci & Boudry, 2011; Rose et al., 2020). Inaccurate metaphors lead to inaccurate understanding of gene-editing technology by the public as well as policy makers (O’Keefe, et al., 2015). However, metaphors as a tool are indispensable in science communication (O’Keefe et al., 2015; Taylor & Dewsbury, 2018).
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Framing
As a means to promote a problem, causal interpretation, moral evaluation or recommendation in communication, framing is used to select of a portion of a perceived reality and make it more salient or prominent in a communication (Entman, 1993;
Goffman, 1974). Framing as a theory of media effects suggests how an issue is characterized in news influences how the public understands the issue (Schuefele &
Tewskurg, 2007). The frame serves as an organizing abstract or concrete cue meant to help facilitate audience categorization, labeling, interpreting and evaluating the information. When communicators choose words, visuals, sources and other framing elements, the result is a frame used as a tool to convey information to an audience (Price
& Tewksbury, 1997). The intention is to express the message in a way the audience can receive it, relate to it, and interpret it. The audience does this by using schemas, meaning framing is based in cognitive applicability (Schuefele & Tewskurg, 2007). The audience draws on schemas to interpret and connect with the issue (Price & Tewksbury, 1997).
Framing tells the audience how to think about a topic by providing a particular perspective to describe it (Entman, 1993; 2004).
Previous framing research indicates news coverage of agrobiotechnology is cyclical and event driven in nature, favoring new technology on the outset then emphasizing disfavor following biosafety or food safety risk events (Marks &
Kalaitzandonakes, 2001). Metaphors have been identified as framing tools in news coverage to represent complex scientific concepts, including gene-editing, that then shape subsequent regulatory and ethical schemas used by the audience to interpret the information (O’Keefe et al., 2015).
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Elaboration Likelihood Model
Persuasion of an individual can be achieved through the central, systematic processing route and the peripheral, heuristic processing route (O’Keefe, 2016). The
Elaboration Likelihood Model operates under the assumption that both routes do not occur at the same time (Petty & Cacioppo, 1986). “The ELM is based on the notion that people are motivated to hold correct attitudes but have neither the resources to process vigilantly every persuasive argument nor the luxury–or apparently inclination–of being able to ignore them all” (Cacioppo et al., 1986, p. 1032). The low elaboration of the peripheral processing route occurs when the receiver either does not have the motivation and ability to process the communication or has the motivation and ability but fails to utilize deep cognitive thought. In both of these scenarios, the receiver uses peripheral cues, or heuristics, to process the message to maintain their attitude or produce a weak attitude change (Petty & Cacioppo, 1986). Deep cognitive consideration, or high elaboration, of a persuasive message is more likely to achieve an enduring attitude that predicts behavior. The model suggests the high elaboration of the central processing route is possible only when a message receiver has the motivation and ability to process the communication (Petty & Cacioppo, 1986; Petty et al., 2009).
A persuasive message factor, metaphors are a linguistic tool used to craft intense, powerful language that has been shown to be more persuasive than powerless language
(Perloff, 2008). The use of metaphors in language increases persuasiveness of a message by piquing the receiver’s interest in the message, increasing motivation to process the message and evoking a greater number of schemas to do so than a strictly literal message
(Sopory & Dillard, 2002). These characteristics of cognitive processing indicate
13 Texas Tech University, Nellie Hill, August 2020 metaphors can encourage the use of central processing to validate a message. In short,
“metaphors enhance persuasion” (Sopory & Dillard, 2002, p. 382).
Structure-Mapping Theory
Gentner (1982) stated that a metaphor “asserts that identical operations and relationships hold among nonidentical things” (p. 4). Structure-mapping theory argues metaphors as a system of connected knowledge which is used by a message receiver to map relations from a known topic to an unknown topic until a maximum structural match between the two topics has been reached (Gentner, 1982; 1983; 1989; Gentner &
Bowdle, 2008). Metaphors work by drawing comparisons between the relational similarities held by the familiar concept to the novel concept (Sopory & Dillard, 2002).
Sopory and Dillard explained, “For example, the metaphor ‘Encyclopedias are goldmines’ is interpreted by noting the common relation ‘valuable nuggets found by digging’ rather than the independent similar attributes ‘valuable nuggets’ and ‘dig’” (p.
384).
Structure mapping occurs between a base system, the known domain, and a target system, the unknown domain (Gentner, 1982). Good scientific metaphors begin with
“base specificity” (Gentner, 1982, p. 12), meaning a known domain that is explicitly understood by the receiver of the metaphorical message. Therefore, a base system is most often a topic familiar to the receiver as this piece alone is the most critical to enabling structure-mapping. A good scientific metaphor has the structural characteristics of clarity, richness, abstractness, and systematicity (Gentner, 1982). Finally, the scope and validity of a scientific metaphor must be considered. The scope of a metaphor determines its usefulness in application to scientific topics. The validity of a message is how true the
14 Texas Tech University, Nellie Hill, August 2020 operations and relationships of the base system are of the target system (Gentner, 1982).
These features of a metaphor combine to enable a receiver to map meaning from the known to the unknown scientific topic.
Metaphors are a critical component of science communication as they cause more elaboration and persuasion than literal messages (O’Keefe et al., 2015; Sopory & Dillard,
2002; Taylor & Dewsbury, 2018; Van Stee, 2018). Researchers have explained the persuasive effects of metaphors in terms of attention, communicator credibility, relief, reduced counterarguments, stimulated elaboration, superior organization, and resource matching (Van Stee, 2018). Using structure-mapping theory as a framework, Whaley
(1991) proposed metaphors encourage the message receiver to utilize a greater number of memories to map meaning from the base system to the target system. Yet, it is unknown which metaphors regarding gene editing are most effective (O’Keefe et al., 2015; Rose et al., 2020; Sopory & Dillard, 2002).
Research Design and Rationale
A sequential, mixed-method research design was utilized for this study. Mixed- methods, utilizing qualitative and quantitative research traditions, can be combined in any or all stages of a multi-phased study (Ary et al., 2010). The overarching purpose of the study guides the use of blended “paradigms, philosophical assumptions, and theoretical perspectives” required of mixed-method design (Ary et al., 2010, p. 561). The purpose of using three unique methods in this study was developmental – to use the findings from one study to guide the next (Ary et al., 2010). This allowed investigation of user- generated content regarding gene-editing applications in agriculture, analysis of media
15 Texas Tech University, Nellie Hill, August 2020 content regarding the topic, and finally using these findings to test messages on members of the public.
This study utilized two quantitative methods (descriptive analysis and experimental survey research) and one qualitative method (systematic metaphor analysis). The first stage of the study was a descriptive analysis of publicly available
Twitter content regarding gene-editing applications in agriculture collected between
September 1, 2018 and December 31, 2019. Meltwater, a social media monitoring tool, collected metrics and assigned a sentiment (positive, negative, or neutral) to each tweet.
Descriptive statistics were used to analyze metrics of reach, engagement, and sentiment for all tweets.
The second stage of the study was a qualitative systematic metaphor analysis of online news media articles concerning gene-editing in agriculture found in four U.S. national news outlets: the New York Times, Washington Post, The Atlantic, and The
Associated Press. Articles published between January 2, 2015 and June 11, 2019 were collected using the Nexi Uni database. Using a data-driven coding process, the metaphors in the news articles were identified to create, evaluate, and modify a list of metaphorical concepts for final analysis (Andriessen & Gubbins, 2009; O’Keefe et al., 2015; Schmitt,
2005; Schreier, 2012).
The final stage of the study employed an experimental treatment embedded in a survey guided by the findings of the descriptive analysis and the systematic metaphor analysis phases. Mock online news articles were created to explore the persuasive effects of elaboration and willingness to share the information on social media of metaphorical
16 Texas Tech University, Nellie Hill, August 2020 concepts used to describe gene-editing applications in agriculture. ANCOVAs were used to determine if there were significant differences between treatment groups.
Scope of Investigation, Assumptions, and Limitations
The scope of this study included publicly available Twitter accounts that posted content relating to gene-editing applications in agriculture between September 1, 2018 and December 31, 2019. In addition, the investigation examined online news media articles on the same topic, but published between January 2, 2015 and June 11, 2019 by the New York Times, Washington Post, The Atlantic, and The Associated Press.
Participants in the experimental study were sought out to be a nationally representative sample of the population as gathered by Qualtrics, an online survey building and distribution platform.
The study was completed under several assumptions. During the first stage, it was assumed Twitter provided a suitable amount of public discussion regarding gene-editing applications in agriculture to represent public opinion on the topic. During the second stage, it was assumed the legacy media outlets of New York Times, Washington Post, The
Atlantic, and The Associated Press adequately reported on the topic enough to influence public opinion. Finally, during the third study, it was assumed the sample of the U.S. population provided by Qualtrics was nationally representative and that they responded honestly to the instrument items.
Limitations of this study include time in that tweets were only collected over a 15 month period, and news media articles were only collected during a three and a half-year period. While the time frame does capture changes and events over the selected period, extension of the time frame would have allowed for the public to further engage in the
17 Texas Tech University, Nellie Hill, August 2020 conversation and journalists to further report on the topic. The survey was disseminated during the year following Twitter and news media data collection, limiting comparison of public opinions across the phases of the study. In addition, the survey was given during the novel coronavirus (COVID-19) outbreak. This may have made the topic less prevalent in the minds of participants.
18 Texas Tech University, Nellie Hill, August 2020
CHAPTER II
A DESCRIPTIVE ANALYSIS OF TWITTER CONTENT REGARDING THE USE OF GENE-EDITING IN AGRICULTURE
Abstract
Gene-editing technology is the next chapter in the storied history of agriculture’s early adoption of new technologies. This disposition is born out of a need to overcome the demanding challenge placed on an industry to address growing global food demand while reducing the use of resources and impacts on the environment. The rise of DNA- free gene-editing technology in recent years has given scientists a new avenue for improving animal and crop production within these conservation efforts. As people are forming their opinion about gene-editing applications in agriculture, they are turning to social media to seek and share information and opinions on the topic. There is a need to understanding how the public discusses the technology so effective messaging can be created. The purpose of this study was to describe the characteristics of Twitter content related to applications of gene-editing in agriculture. A Meltwater social media monitor collected N = 13,189 relevant tweets for analysis. Between September 1, 2018 and
December 31, 2019, the amount of conversation regarding gene-editing in agriculture, the number of contributing Twitter users, and the reach of the conversation was relatively stable. In contrast, engagement with the conversation was on the rise and the sentiment of tweets was becoming increasingly positive. Public accounts with the most reach were predominantly news organizations, while accounts with the greatest engagement were a mix of news accounts and personal accounts. Recommendations for research and practice are also provided.
19 Texas Tech University, Nellie Hill, August 2020
Introduction
The agricultural industry has a storied history of early adoption of new technologies and it continues with advancements in genetic engineering (Goold et al.,
2018). This disposition is born out of a need to overcome the demanding challenge placed on an industry to address growing global food demand. This challenge is compounded by public calls for reducing the use of resources and increasing measures to protect environmental impacts of agricultural practices. The rise of DNA-free gene- editing technology in recent years has given scientists a new avenue for improving animal and crop production within these conservation efforts (Shew et al., 2018).
Although targeted genome editing has been used in labs since the early 1980s, the modern boom in DNA-free gene-editing refers to two technologies: Transcription
Activator Like Effector Nucleases (TALENs) and Clustered Regularly Interspaced Short
Palindromic Repeats (CRISPR)-Systems (Metje-Sprink et al., 2019). The ease and accuracy of implementation associated with modern gene-editing coupled with the ability to avoid the use of DNA has caused great excitement and debate in the scientific community and the public regarding the potential impacts of the technology on food
(Brossard, 2018; Molteni, 2019; Shew et al., 2018).
Among the enthusiasm for the potential of modern gene-editing technology, there are social, cultural, and ethical debates regarding the technology’s implications
(Brossard, 2018). In addition, the conversations may be muddied by the public’s confusion regarding the differences between genetically modified food and food enhanced by gene-editing as well as a lack of knowledge around the science of gene- editing (Ishii & Araki, 2016; Lusk et al., 2018; McFadden & Lusk, 2016; Miller, 2004;
20 Texas Tech University, Nellie Hill, August 2020
Rainie, 2017; Zerbe, 2004). Amid these debates and potential confusion, scientists continue to advance gene-editing technology in plants and animals. The increasing power and capability of gene-editing elicits greater visibility and conversation around the technology (Molteni, 2019).
Such discussions take place, in part, online as access to the internet and consumer preference to use it as a source of information continue to increase (Brossard &
Shanahan, 2017). Social media platforms play an significant role in viewing and sharing science information as 33% of Americans consider it an important way to get science news (Funk et al., 2017). Roughly seven in ten Americans have a social media account.
Of those who have an account, half report science news appears on their social media feeds (Funk et al., 2017). When they see science news posts, 54% of individuals will click the external link if one is included. However, these posts are most likely not from a science-based accounts because 73% of individuals do not follow such an account (Funk et al., 2017).
Nevertheless, one of the first sources the public turns to for information in the midst of a controversial topic or issue is social media (Gil de Zuniga et al., 2012). The more people discuss an issue, the more confident they feel in expressing their ideas and affecting change (Finkel, 1985; Smith, 1999). Social media outlets are a desirable place to have these discussions because information can be quickly shared to a large number of people. This interconnectedness allows information to reach people who might not otherwise have seen it (Nahon & Hemsley, 2013).
Using social media to seek information has been associated with increased engagement and activity in online and offline communities as well as civic and political
21 Texas Tech University, Nellie Hill, August 2020 participation (Gil de Zuniga et al., 2012). The structure of social media platforms allows for gathering information, as well as discussing it with other members of the online community, leading to greater elaboration and reflection. This process creates community for the user and has been found to lead to offline community engagement through civic action (Gil de Zuniga et al., 2012).
Credibility and personal interest are important factors social media users consider when exploring such platforms for information and deciding what to share to their own network of followers (Metaxas et al., 2015). People who share information on social media want to shape their online identity as being smart, helpful, and informed (Berger,
2014; Boyd & Ellison, 2007). Still, Varma et al. (2017) found people are more honest about their opinions on social media than they are in-person.
Twitter is of particular interest for examining relationships within food systems as its structure and norms defy those of other social media platforms. The diverse usership able to view public accounts and the continuous sharing of tweets disassemble audience boundaries to construct highly individual accounts contributing to the community at large
(Pennell, 2016). Twitter users follow other accounts based on particular interests, not reciprocity or social connectedness (Smith et al., 2012). Information is sought out through cognitive means such as reading articles, not social means such as asking others
(Hughes et al., 2012).
Stigmas, misrepresentations, and the uproar characteristic of public opinion about genetically modified organisms could carry over to products of gene-editing, hampering their potential to meet the challenge of global food demand (Huang et al., 2016; Ishii &
Araki, 2016; Shew et al., 2018). While these products have not yet reached retails shelves
22 Texas Tech University, Nellie Hill, August 2020 and the public is still forming their opinion of gene-editing applications in agriculture, there is an opportunity to gain insight into the trajectory of public opinion and make adjustments in communications strategies going forward (Brossard, 2018; Miller, 2004;
Rainie, 2017). Science communicators and agriculturalists have a rare opportunity to proactively stave off negative public opinion in favor of public appreciation for the complexities of the food system (Charleston|Orwig, 2017; Genetic Literacy Project,
2016). With an understanding of how the public discusses gene-editing applications in agriculture, communicators can develop strategies to promote the positive implications of the technology (Brossard, 2018).
Literature Review
Twitter Characteristics and Uses
Politicians, celebrities, brands, media, individual members of the public, and many other walks of life are on Twitter, the social media platform with more than 330 million monthly active users (Clement, 2019). Of those users, 126 million are active on the platform daily (Shaban, 2019). In 280 characters or less, users can share information in the form of URLs, photos, videos, and text via tweets. Other than these constraints, users are encouraged to discover and engage with the platform by crafting and posting their message, which can be replied to, retweeted, or liked by anyone on the platform so long as the user’s tweets are public (Twitter, 2020).
According to the Pew Research Center (Wojcik & Hughes, 2019), approximately
22% of adult Americans use Twitter. The median age of a Twitter user is 40 years old, while the median age of a U.S. adult is 47 years old. Twitter users have slightly higher educational attainment than the general U.S. population, with 42% of adult Twitter users
23 Texas Tech University, Nellie Hill, August 2020 having bachelor’s degree while 31% of the adult U.S. population has the same degree. Of adult Twitter users who reported their annual household income, 41% said it was higher than $75,000, while 31% of the general population’s annual household income is above that dollar figure (Wojcik & Hughes, 2019). These characteristics, coupled with social media users’ desire to portray an online identity that is smart, helpful, and informed, make Twitter users prime candidates to lead acceptance of new technologies (Berger,
2014; Boyd & Ellison, 2007; Rogers, 2003).
Of the one in five U.S. adults on Twitter, 71% use it to get news (Wojcik &
Hughes, 2019). Among this news is the growing subset of science-related information
(You, 2014). Sharing science news on social media is a means of demystifying the topic for the public, making people more aware of science issues and making the conversation around issues more diverse (Busquet & Viken, 2019). While there are more than 45,000 scientists using Twitter, most Twitter users do not follow a science account (Funk et al.,
2017; Ke et al., 2017). Instead, users likely get their science news from general news outlets who have a significant role in the content shared on the platform (Wojcik &
Hughes, 2019). Twitter remains an excellent platform for bridging and encouraging engagement among the public and science, but there is a need to understand how science is being discussed on the platform so communicators can provide content that meets the norms and needs of the platform (López-Goñi & Sánchez-Angulo, 2018).
Social Media Monitoring and Gene-Editing on Twitter
Social media monitoring gathers content publicly shared on such platforms as a means of determining how a topic of interest is discussed by platform users (Zhang &
Vos, 2014). Lumoha-aho and Vos (2010) found early understanding of social media
24 Texas Tech University, Nellie Hill, August 2020 discussion around an issue assisted in predicting the growth of the issue. Numerous social media monitoring platforms exist, but most provide descriptive information about content including reach, engagement, sentiment, and user information. These metrics can be used to gain a deeper understanding of the diffusive nature of the topic under study and how it is being discussed (Zhang & Vos, 2014).
Researchers argue the reasons people share information on social media include creating an online identity with good judgement and sophistication, establishing social bonds, and attracting attention (Berger, 2014; Boyd & Ellison, 2007; Cappella et al.,
2015; McLaughlin et al., 2016). Meng et al. (2018) and Zhu et al. (2020) found perceived negative emotion communicated by a tweet was a significant, positive predictor of sharing, expediting the reach of and engagement with the information throughout the social system. Zhu et al. (2020) specifically investigated tweets from the Centers for
Disease Control over a 12-month period. They found tweets with severity, efficacy, and call-for-action information were shared and disseminated more rapidly than tweets without those characteristics (Zhu et al., 2020).
Previous research has examined Twitter users’ perception of human applications of gene-editing (Yabar et al., 2018). The researchers gathered tweets containing the word
“CRISPR” posted in time frames around five human gene-editing news events between
December 2015 and November 2017. Analyzing a sample of 500 randomly selected tweets, the researchers found 91% of tweets were neutral in sentiment regarding CRISPR.
More than half of the tweets (65%) originated from news outlets including articles, blogs, and scientific journals. No significant difference in sentiment of the tweets was found between those posted before and those posted after the event (Yabar et al., 2018). A
25 Texas Tech University, Nellie Hill, August 2020 review of the literature does not reveal a similar study specific to agricultural applications of gene-editing.
Conceptual Framework
Diffusion of innovation theory served as the conceptual framework for this study.
This theory posits members of a social system spread information about an innovation through two-way communication using various channels over time (Rogers, 2003). This diffusion of innovation process can occur online or in-person. The social system in this study is online, specifically Twitter. As information is exchanged in the social environment, individuals utilize the innovation-decision process to either converge or diverge on the innovation adoption decision.
The innovation-decision process is characterized by the following stages: (a) knowledge, (b) persuasion, (c) decision, (d) implementation, and (e) confirmation. An individual becomes aware of the innovation, forms a perspective on it, decides whether to adopt or reject it, takes action on their decision, then seeks validation the right decision was made (Rogers, 2003). Social media use is increasingly used for knowledge sharing
(Ahmed et al., 2019). Information perceived as useful and influential to others is more likely to be shared by social media users (Berger, 2014). Research utilizing diffusion of innovation theory has found social media sharing behaviors indicate the sender’s approval or acceptance of the information (Kee et al., 2016).
Inherent and unique to the innovation-decision process is the uncertainty associated with the innovation as it is new and unknown, lacking predictability, structure, and information. The uncertainty factor is taken into account by the individual during their decision-making process about the innovation. Gathering information is one way
26 Texas Tech University, Nellie Hill, August 2020 uncertainty can be reduced (Rogers, 2003). The complexity and far-reaching social, health, economic and national security implications of gene-editing technology make the innovation rife with uncertainty for many (Scheufele et al., 2017). With so many avenues of informational needs, people turn to communication channels, including social media and news media, to gather information to reduce their uncertainty (Rogers, 2003;
Scheufele et al., 2017).
Communication channels carry information to members of the social system
(Rogers, 2003). These channels are critical pathways individuals use to gather uncertainty-reducing information during the knowledge and persuasion stages of the innovation-decision process. However, individuals may have difficulty discerning the source originating the message and the channel by which it is delivered to them (Rogers,
2003). Therefore, Rogers (2003) suggested sources and channels may be used interchangeably. For the purpose of this study, the source is the Twitter user and the channel is the Twitter platform.
Sources and channels are categorized as either mass media or interpersonal; cosmopolite or localite. Mass media sources and channels represent a mass medium, such as newspapers, able to quickly reach a large audience with information. Mass media are able to raise awareness of an innovation and influence weakly held attitudes toward it.
Interpersonal sources and channels represent two-way communication with peers able to exchange and inquire about clarifying information. Interpersonal communication is able to persuade individuals with strongly held attitudes. Given these attributes, mass media hold greater influence in the knowledge stage of the innovation-decision process, whereas
27 Texas Tech University, Nellie Hill, August 2020 interpersonal sources and channels have greater influence in the persuasion stage
(Rogers, 2003).
Mass media sources and channels are almost always cosmopolite, linking members of the social system to resources outside of the social system. Interpersonal sources and channels may be cosmopolite or localite, either linking members to outside resources or resources already present within the social system (Rogers, 2003). Rogers
(2003) argued cosmopolite sources and channels play a greater role in the knowledge stage; whereas, localite sources and channels have a greater role in the persuasion stage.
Meng et al. (2018) utilized diffusion of innovation theory to explore the different roles mass media and interpersonal connections contribute to communication patterns on
Twitter to support innovation adoption. The researchers found that while mass media channels widely disseminate information via a tweet because of their large user following, interpersonal connections serve as brokers to share information across communities. Tweets from brokers were retweeted by users more often, increasing the virality of the information. Perhaps this is due to the novelty and relevancy of the information to the user (Meng et al., 2018).
Zhu et al. (2020) also utilized diffusion of innovation theory to explore information sharing on Twitter. In their study, the researchers identified the characteristics of tweets from the Center for Disease Control that were shared quickly and widely on Twitter. Tweets with severity, efficacy, and call-for-action information were shared more rapidly and diffused to a greater number of receivers. In addition, tweets with a negative tonality were shared faster and wider than positively toned tweets.
Tweets with fewer affiliative words (e.g., ally, together, and friend) were also shared
28 Texas Tech University, Nellie Hill, August 2020 more rapidly and to a greater number of receivers. The researchers concluded the characteristics of tweets have significant effects on diffusion outcomes (Zhu et al., 2020).
There is a need to understand the current state of public discourse regarding gene- editing applications in agriculture. An examination of the Twitter content regarding gene- editing applications in agriculture will help agricultural and science communicators gain a better understanding of the diffusion features of the innovation on the social media platform. This will enable practitioners to improve their own communications strategies around advancements in the technology.
Purpose and Research Questions
The purpose of this study was to describe the characteristics of Twitter content related to applications of gene-editing in agriculture. The study was guided by the following research questions:
RQ1: How many mentions of gene-editing applications in agriculture were
publicly posted on Twitter between September 1, 2018 and December 31, 2019?
RQ2: What was the social reach and engagement of those tweets?
RQ3: What was the sentiment of those tweets?
RQ4: How does tweet reach and engagement vary based on tweet sentiment?
Methodology
Social media monitoring was utilized to facilitate a quantitative, descriptive analysis of content related to gene-editing applications in agriculture publicly posted on
Twitter between September 1, 2018 and December 31, 2019. Meltwater, a social media monitoring platform, was used to collect the relevant content. A monitor was established within Meltwater using Boolean search query to identify only content related to gene-
29 Texas Tech University, Nellie Hill, August 2020 editing applications in agriculture within the designated time frame. Data were analyzed for the number of tweets, their reach, engagement, and sentiment.
Sampling
The keywords included in the Meltwater monitor were based on an in-depth scan of the literature and the various terminology used to discuss gene-editing (Huang et al.,
2016; Johnson, 2015; Tagliabue; 2015; Yabar et al., 2018). A review of the scholarly and popular press literature regarding gene-editing led to a Meltwater monitor search for the root keywords “CRISPR”, “gene-editing”, “gene editing”, and “genome editing”, and
“TALENs”. Each root keyword also required at least one of the following keywords:
“agriculture”, “animal”, “livestock”, “crop”, “food”, and “plants”. The monitor excluded tweets with the keywords “baby”, “babies”, and “human”. The complete Boolean search was: ((CRISPR OR gene-editing OR "gene editing" OR "genome editing" OR TALENs)
AND (agriculture* OR animal* OR crop* OR food* OR livestock* OR plant*)) NOT
(baby* OR babies OR human*). The asterisks indicate a wildcard search for the term, meaning the monitor will return content in an alternative form of the keyword. For example, “plant*” will also return tweets with “plants” and “planting” if the tweet matches the rest of the search requirements.
Data Collection
The monitor collected tweets published between September 1, 2018 through
December 31, 2019. Data collection was not possible prior to September 1, 2018 as
Meltwater only maintains a rolling 15 months of social media content. Collection concluded at the end of December 2019 due to the volume of tweets collected and the
30 Texas Tech University, Nellie Hill, August 2020 timeframe to complete the study. The monitor gathered N = 13,189 tweets pertaining to the study during this time period.
The monitor returned any tweets posted by public Twitter accounts mentioning the search terms. Meltwater creates reports containing each tweet and extensive information associated with it, including the date and time the tweet was published, username of the publisher, full text of the tweet, country of origin, reach of the post
(number of followers of the post author), engagement with the post if it is an original tweet (number of replies, retweets, and likes), and sentiment (overall positive, negative, or neutral tonality) (Meltwater, 2017). Sentiment is analyzed by a Natural Language
Processing Computational Linguistics algorithm to assess the opinion, sentiment and emotions of a text and categorize it as positive, negative or neutral (Kadam & Joglekar,
2013).
Data Analysis
The descriptive data available from Meltwater was exported into Microsoft Excel spreadsheets the data was cleaned to only include tweets originating from the United
States, leaving N = 13,189 tweets for analysis. Data were limited to the United States because definitions and regulations regarding gene-editing differ from country to country
(Metje-Sprink et al., 2019). Microsoft Excel was used to tabulate descriptive measurements of the data. Data were exported to IBM SPSS v. 25 for statistical analysis.
Descriptive and non-parametric statistics were used to address the research questions.
Statistical significance was set a priori at <.05 (Field, 2017).
31 Texas Tech University, Nellie Hill, August 2020
Results
RQ1: How many mentions of gene-editing applications in agriculture were publicly posted on Twitter between September 1, 2018 and December 31, 2019?
Between September 1, 2018 and December 31, 2019, there were N = 13,189 mentions of gene-editing related to agriculture on Twitter. Of those, n = 3,576 were posted in the last quarter of 2018 and n = 9,614 were posted in 2019. Those tweets were publicly posted by N = 5,824 unique users. Figure 2.1 displays the changes in frequency of tweets and unique users contributing to the conversation by month during the search period for the study.
Figure 2.1
Frequency of Gene-editing in Agriculture Tweets and Unique Twitter Users Contributing
-- Tweets -- Unique Users 1,157 1,152 1,007 1,018 911 898 832 847 768 816 758 749 707 755 640 663 579 605 589 567 610 543 560 508 429 461 475 429
331 347 347 332 Frequency
Month 2018 - 2019
RQ2: What was the social reach and engagement of those tweets?
Tweets pertaining to the study posted between September 1, 2018 and December
31, 2019 had the potential to reach 266,554,740 Twitter users. Reach is the number of
Twitter users who may see the tweet, calculated based on the number of followers of the
32 Texas Tech University, Nellie Hill, August 2020 post author (Meltwater, 2017). Peak reach occurred in May 2019 with 147,919,414 potential viewers of tweets regarding gene-editing in agriculture. Figure 2.2 presents the changes in reach during the time period of the study.
Figure 2.2
Reach of Gene-editing in Agriculture Tweets
160,000,000 140,000,000 120,000,000 100,000,000 80,000,000
60,000,000 Reach Volume Reach 40,000,000 20,000,000 0
Month 2018 - 2019
As the reach of a tweet is calculated based on the number of followers of the post author, the top ten Twitter accounts in terms of reach are presented in Table 2.1.
33 Texas Tech University, Nellie Hill, August 2020
Table 2.1
Gene-editing in Agriculture Top 10 Twitter Accounts with Regards to Reach
Account Name Account Type Reach nytimes News Media 43,472,723 WIRED News Media 10,349,781 ScienceNews News Media 2,814,784 businessinsider News Media 2,586,572 WIREDScience News Media 2,013,565 TheAtlantic News Media 1,828,854 CNET News Media 1,621,044 sciencemagazine News Media 1,246,320 NYTScience News Media 1,150,285 RogueNASA Personal Account 868,282
Original tweets pertaining to the study posted between September 1, 2018 and
December 31, 2019 resulted in a total of 24,067 engagements, which are replies, retweets, and likes associated with an original tweet (Meltwater, 2017). Meltwater does not calculate engagement for retweets. Average engagement with individual tweets was 7.43
(SD = 17.69). Engagement with individual tweets ranged from zero to 452. Peak engagement occurred in May 2019 with 2,380 replies, retweets and/or likes associated with tweets regarding gene-editing in agriculture. Figure 2.3 presents the engagement with tweets and the frequency of tweets during the time period of the study.
34 Texas Tech University, Nellie Hill, August 2020
Figure 2.3
Frequency and Engagement with Gene-editing in Agriculture Tweets
2,380 2,287 -- Tweets -- Engagement 2,023 1,957
1,683 1,621 1,631 1,449 1,412 1,270 1,301 1,255 1,157 1,152 1,146 1,018 1,007 953 887 911 898 832 847 812 768 816 Frequency 707 755 610 579 543 589
Months 2018-2019
External to Meltwater’s reporting, the proportion of retweets to original tweets was calculated as an indicator of interaction over the entire search (Grabbert et al., 2019).
Of the N = 13,189 tweets collected, n = 7,022 (53.2%) were retweets, indicating high interaction among accounts participating in the conversation about gene-editing in agriculture (Grabbert et al., 2019).
Engagement is indicative of approval or acceptance of the information by the user replying, retweeting or liking the content (Kee et al., 2016). The top 10 Twitter posts in terms of engagement are presented in Table 2.2.
35 Texas Tech University, Nellie Hill, August 2020
Table 2.2
Gene-editing in Agriculture Top 10 Twitter Posts with Regards to Engagement
Account Name Tweet Engagement ajitjohnson_n Researchers used CRISPR before birth in an animal 452 model to treat a lethal lung disease that causes death within hours after birth. This study shows that in utero editing could be a promising new approach for treating fatal diseases before birth. https://t.co/qqC4YeIUMo https://t.co/i1FtvMnwmz nytimes The world's first Crispr snails might help clear up a 334 mystery of left/right asymmetry in the animal kingdom https://t.co/GwZxifW4CR nytimes The world's first Crispr snails might help clear up a 320 mystery of left/right asymmetry in the animal kingdom https://t.co/ptdPhaaSFt nytimes The world's first Crispr snails might help clear up a 243 mystery of left/right asymmetry in the animal kingdom https://t.co/R3EIWOXcrL
Incarnated_ET Scientists cure mice of HIV for first time in 209 groundbreaking study using CRISPR A group of scientists have, for the first time, eliminated HIV DNA from the genomes of living animals, in what is being described as a critical step towards developing a cure for the AIDS virus.
AgBioWorld Japan understands that gene-editing like #CRISPR 184 is not GMO, just plain old mutagenesis with knowledge & precision! So, Genome-edited food products to go on sale in Japan, minus scary labelling or unnecessary regulatory burden https://t.co/VpbSW4PkMu
AgBioWorld Whoa! A major breakthrough in sorghum, gene 165 editing has elevated the protein of this important crop from 9-10% to a staggering 15-16%. Also improved digestibility. A big deal for Africa & India, another reason to embrace NBT, remove regulatory hurdles https://t.co/2WBlEECemA
36 Texas Tech University, Nellie Hill, August 2020
Account Name Tweet Engagement IDSAInfo Is #HIV a curable disease? A new study suggests 122 yes, as researchers find success in eliminating the disease from an infected animal’s genome through a combo of modified ARV treatment & gene- editing tool CRISPR-cas9. https://t.co/hrqZc4hATU #EndHIVEpidemic
WIREDScience Crispr works in almost every animal that scientists 121 have tried, from silkworms to monkeys, and in just about every cell type—kidney cells, heart cells, you name it. What’s more, Crispr is both fast and cheap. So how far do we want it to go? https://t.co/uueH6Bqbh0
BioBeef So I binge watched #UnnaturalSelection on Netflix 117 last night 'cause that is what animal geneticists with an interest in #scicomm do on a Friday night, & have some thoughts around agricultural applications of genome editing so I wrote a BLOG @ucanr @ucdavis https://t.co/pUb0jEPSC7
RQ3: What was the sentiment of those tweets?
Meltwater’s natural language processing algorithm assigned a sentiment of positive, negative, or neutral to each tweet based on the overall tonality of the message
(Meltwater, 2017). Of all collected tweets (N = 13,189), Meltwater coded 5,083 (38.5%) as positive, 1,840 (14.0%) as negative, and 6,266 (47.5%) as neutral in tonality. Peak positive sentiment occurred in November 2019 with 64% of tweets during the month having an overall positive tonality. Peak negative sentiment occurred in February 2019, with 52% of tweets during the month having an overall negative tonality. Peak neutral sentiment occurred in August 2019, with 62% of tweets during the month having an overall neutral tonality. Figure 2.4 presents the sentiment of tweets during the time period of the study. 37 Texas Tech University, Nellie Hill, August 2020
Figure 2.4
Sentiment of Gene-editing in Agriculture Tweets
100% % Positive 90% % Negative 80% % Neutral 70% 60% 50% 40% 30%
Sentiment Sentiment Percentage 20% 10% 0%
Month 2018-2019
RQ4: How does tweet reach and engagement vary based on tweet sentiment?
A Kruskal-Wallis H test was utilized to address research question four as a visual inspection of a boxplot indicated outliers too extreme to allow for ANOVA statistical analysis, but also too valuable to be removed from the data set (Field, 2017; Laerd,
2015). As such, a Kruskal-Wallis H test was run to determine if there were differences in tweet reach between three groups of tweet sentiment: positive (n = 5,083), negative (n =
1,511), and neutral (n = 6,595). Distributions of reach were similar for all groups, as assessed by visual inspection of a boxplot. Median reach was statistically significantly different between groups, χ2(2) = 8.279, p = .016. To determine differences between groups, pairwise comparisons were performed using Dunn's (1964) procedure with a
Bonferroni correction for multiple comparisons. Adjusted p-values are presented. Post 38 Texas Tech University, Nellie Hill, August 2020 hoc analysis revealed statistically significant differences in median tweet reach between positive sentiment (Mdn = 944.00) and neutral sentiment (Mdn = 1011.00) tweets (p =
.023), but not between positive sentiment and negative sentiment (Mdn = 915.00) (p =
1.00), or negative sentiment and neutral sentiment (p = .211).
Meltwater does not tabulate engagement for retweets, so 7,022 retweets were removed from the population. In addition, 2,928 tweets received no replies, retweets or likes and so this mass of outliers were removed from the data set in order to determine if a difference in sentiment elicits a difference in amount of engagement. These steps left a sample of n = 3,239 tweets for statistical analysis. A Kruskal-Wallis H test was utilized to address research question four as a visual inspection of a boxplot indicated outliers too extreme to allow for ANOVA statistical analysis, but also too valuable to be removed from the data set (Field, 2017; Laerd, 2015). As such, a Kruskal-Wallis H test was run to determine if there were differences in tweet engagement between three groups of tweet sentiment: positive (n = 1,285), negative (n = 417), and neutral (n = 1,537). Distributions of reach were not similar for all groups, as assessed by visual inspection of a boxplot.
Engagement was statistically significantly different between sentiment groups, χ2(2) =
14.650, p = .001. To determine differences between groups, pairwise comparisons were performed using Dunn's (1964) procedure with a Bonferroni correction for multiple comparisons. Adjusted p-values are presented. Post hoc analysis revealed statistically significant differences in tweet engagement between positive sentiment (mean rank =
1696.15) and neutral sentiment (mean rank = 1573.77) tweets (p = .001), as well as between positive sentiment and negative sentiment (mean rank = 1555.75) (p = .021), but not negative sentiment and neutral sentiment (p = 1.00).
39 Texas Tech University, Nellie Hill, August 2020
Discussion, Conclusion & Recommendations
As people are forming their opinion about gene-editing applications in agriculture, they are turning to social media to seek and share information and opinions on the topic (Gil de Zuniga et al., 2012; Hughes et al., 2012). Communications strategies promoting the positive implications of gene-editing applications in agriculture rest on an understanding of how the public discusses the technology (Brossard, 2018). The purpose of this study was to describe the characteristics of Twitter content related to applications of gene-editing in agriculture.
Over the time period of this study, the amount of conversation regarding gene- editing in agriculture, the number of contributing Twitter users, and the reach of the conversation was relatively stable, though not without peaks and valleys. In contrast, engagement with the conversation appears to be trending up as time goes on. Sentiment appears to be slightly shifting as well, with positive sentiment trending up while negative and neutral sentiments are trending down. Findings suggest users contributing to the conversation are moving through the innovation-decision process, marking their information exchange and decisions with increased participation in the form of replies, retweets and likes as well as greater positivity (Kee et al., 2016; Rogers, 2003). This bodes well for the concern that negative public opinion of genetically modified organisms could carry over to products of gene-editing (Huang et al., 2016; Ishii & Araki, 2016;
Shew et al., 2018). Results indicate the conversation around gene-editing in agriculture is stable yet growing in interaction and positivity.
As communicators will seek to expand the conversation about applications of gene-editing, results indicate mass media channels hold the greatest opportunity to do so.
40 Texas Tech University, Nellie Hill, August 2020
All but one of the top ten accounts with the greatest reach represent a news platform, six of which have a print magazine or newspaper component. Mass media channels, such as these, hold the greatest opportunity to influence knowledge acquisition about gene- editing in agriculture by linking Twitter users to information outside of the social system
(Rogers, 2003). Results indicated positively toned messages tended to reach a wider audience than neutrally toned tweets, but otherwise there were no significant differences between sentiment and tweet reach. This finding adds nuance to the findings of Zhu et al.
(2020) who only examined reach of positively versus negatively toned messages and found negative toned messages to be shared among a wider audience. Additional investigation of how tonality of tweets affects information diffusion is needed (Zhu et al.,
2020).
Engagement with the conversation about gene-editing can be an indicator of interaction among the social system as well as agreement with the message (Grabbert et al., 2019; Kee et al., 2016). Both are goals among communicators who desire to create content that fosters conversation and encourages feedback from the audience. More than half of the tweets in this study were retweets. Higher engagement (a combination of retweets, replies and likes) was found to be associated with positively-toned tweets compared to negative or neutral tweets. As communicators seek to elicit engagement with content regarding gene-editing in agriculture, they should consider crafting messages with an optimistic or affirmative sentiment instead of fearful or indifferent tones.
The top 10 tweets in terms of most engagement were posted by a variety of user types. The most engaging tweet was from cancer geneticist, @Ajitjohnson_n. Other accounts representing individuals were @AgBioWorld and @Biobeef. Mass media
41 Texas Tech University, Nellie Hill, August 2020 accounts were the sources of the remaining top 10 tweets in terms of engagement. This finding aligns with Rogers (2003) assertion that interpersonal communication is more influential in the persuasion stage of the innovation-decision process, indicated by an approving interaction with the tweet. Communicators and scientists should be encouraged to utilize individual accounts to share messages about gene-editing in agriculture as a means of explaining the positive implications of this technology.
Engagement, however, has still relatively low volume compared to the number of tweets and amount of reach of those tweets. Twitter users involved in the conversation about gene-editing in agriculture are likely to be mostly still in the knowledge stage of the innovation-decision process because sentiment remains primarily neutral over time.
Greater positive or negative sentiment could be an indicator of a decision made about gene-editing in agriculture (Rogers, 2003). Therefore, there is ample opportunity and need for communicators to continue to share information to influence the decision process.
Peaks and valleys in the datasets are cause for further inquiry into the information shared during a given month that received the greatest reach or engagement among the gene-editing in agriculture conversation on Twitter. A review of the top tweets in terms of reach and engagement by month reveal timely studies as well as policy speculation and updates regarding applications of gene-editing garner heightened attention. This suggests people are curious about gene-editing and also follow the regulatory journey of its applications. Table 2.3 provides the top tweets in terms of reach and engagement for each month during the life of the study.
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Table 2.3
Highest Reach and Engagement Tweets by Month Regarding Gene-editing in Agriculture
Month and Year Top Reach Tweet Top Engagement Tweet September 2018 @CMichaelGibson: @CMichaelGibson: In first systemic treatment in In first systemic treatment in large animals, Duchenne large animals, Duchenne Muscular Dystrophy mutation Muscular Dystrophy mutation in in dogs reversed by CRISPR- dogs reversed by CRISPR-Cas9 Cas9 https://t.co/OliQzlDHnw https://t.co/OliQzlDHnw via via @genbio @genbio
October 2018 @ScienceNews: @ScienceNews: CRISPR can replay the CRISPR/Cas9 can redo domestication of some plants domestication in fast forward to in fast forward. create new crops. https://t.co/6txc4xCUvb https://t.co/zQ3eNt8gWp
November 2018 @BostonGlobe: @AgBioWorld: The future of farming is Dutch ag minister may allow robots, drones, and gene crop gene editing even though editing. For Big Agriculture, the practice is banned by the anyway. For the little guy? It’s European Commission. Instagram. Wageningen scientist René https://t.co/bGhbNcZTIp Smulders said the European ban was ‘like using a typewriter while the computer has already been invented’ https://t.co/1O1xLPcjNV
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Month and Year Top Reach Tweet Top Engagement Tweet December 2018 @RogueNASA: @GHGGuru: QT @ScienceNews: TIL this is QT @InglesDietitian: Good a long-held dream. Gene editing question: @ImpossibleFoods has yielded hybrid rice plants claims that their plant based that can make cloned seeds burger “bleeds”, which gives it its without plant sex, an early beef like flavor. The IF “blood” is version of a long-held dream. made from leghemoglobin, which https://t.co/Vc6Jlyw3O5 is a GMO product. These burgers are ultra processed foods sold as the sustainable alternative to original beef. ; Frankly I'm baffled as to why some #planrbased or #cellbased protein companies embrace food technology & yet seem to reject #gmo or gene editing which represents advances in agriculture (food) technology. Can someone explain? @GHGGuru @edgeben @kevinfolta @MatthewJDalby
January 2019 @ADevotedYogi: @AgBioWorld: Are you picking up what is #CRISPR genome editing is used being put down? Food Riots, to destroy a virus that lurks Social Security Austerity, bananas grown in Africa! The Corruption In The Federal banana streak virus also Reserve, New CRISPR Dangers integrates its DNA into the https://t.co/gzrZvdHRKB banana’s genome. In Africa, where bananas are a staple food, most fruits have the virus lurking inside https://t.co/WOz6m7Xmy3
February 2019 @WCVB RT @Chronicle5: @Schmitz_Lab: Increase the food supply? Make The Jacobsen team expands the it crispr. We can cut, edit, paste, toolkit for epigenome editing in delete, insert, just like your plants. Testing of cause vs. effect Microsoft program, except it's relationships between DNA in DNA ... make a plant more methylation and expression can drought tolerant or increased commence! Site-specific nutrition @universityofri manipulation of Arabidopsis loci #thefutureoffood using CRISPR-Cas9 SunTag @shaynaseymour systems. @anthonyeverett 7:30 #wcvb https://t.co/HZdTUScLml https://t.co/hGPpeCkGjC
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Month and Year Top Reach Tweet Top Engagement Tweet March 2019 @WIRED: @WIREDScience: Horn-free? Yup. Heat-tolerant? Crispr works in almost every Sure. Flu-proof? Of course. animal that scientists have tried, Gene editing aims to make our from silkworms to monkeys, and food supply kinder and more in just about every cell type— efficient. But it’s struggling to kidney cells, heart cells, you leave the (er) barn. name it. What’s more, Crispr is https://t.co/Ip3Jc1OflY both fast and cheap. So how far do we want it to go? https://t.co/uueH6Bqbh0
April 2019 @businesssinsider: @ajitjohnson_n: We'll be eating the first Crispr'd Researchers used CRISPR before foods within 5 years, according birth in an animal model to treat a to a geneticist who helped lethal lung disease that causes invent the blockbuster gene- death within hours after birth. editing tool This study shows that in utero https://t.co/zLQJ3C9pgt editing could be a promising new approach for treating fatal diseases before birth. https://t.co/qqC4YeIUMo https://t.co/i1FtvMnwmz
May 2019 @nytimes: @nytimes: The world's first Crispr snails The world's first Crispr snails might help clear up a mystery might help clear up a mystery of of left/right asymmetry in the left/right asymmetry in the animal animal kingdom kingdom https://t.co/R3EIWOXcrL https://t.co/GwZxifW4CR
June 2019 @NYTScience: @AgBioWorld: The world's first Crispr snails The world's first Crispr snails might help clear up a mystery might help clear up a mystery of of left/right asymmetry in the left/right asymmetry in the animal animal kingdom kingdom https://t.co/tETy5uHqyf https://t.co/tETy5uHqyf
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Month and Year Top Reach Tweet Top Engagement Tweet July 2019 @NewsfromScience: @Incarnated_ET: Can #CRISPR transform Scientists cure mice of HIV for China's food supply? first time in groundbreaking study https://t.co/7K75bzZ5O1 using CRISPR A group of scientists have, for the first time, eliminated HIV DNA from the genomes of living animals, in what is being described as a critical step towards developing a cure for the AIDS virus.
August 2019 @NewsfromScience @S_Delaney: China's investment in both new QT @EricTopol: Hopefully facilities and ambitious research uncontroversial opinion: it's not projects has helped the country CRISPR or precision medicine or push ahead with #CRISPR machine learning or hyper- modifications of animals. specialized docs that's gonna fix https://t.co/1iKu3k7Z0T this. It's workaday public health stuff. Clean air and water, better food systems, primary care, less racism, less inequality, more voting. ; Life expectancy in the US has dropped 3 years in a row (unprecedented) & is the worst of all 36 @OECD countries. Nice that @BCAppelbaum put it on the economists, but it's really a self-inflicted deep wound
September 2019 @ChuckGrassley: @na_aatalie: Talked w Iowa Pork Producers QT @CNN Friendly reminder abt trade, gene editing that Stanfurd created a public technology, foreign animal health crisis by creating Juul And disease preparedness & labor Berkeley dveloped the challenge groundbreaking CRISPR gene- https://t.co/scNPJNJbze editing technique; The US Food and Drug Administration has warned leading e-cigarette maker Juul Labs about illegally marketing its product as a safer alternative to cigarettes. https://t.co/jYBavZr9R2
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Month, Year Top Reach Tweet Top Engagement Tweet October 2019 @TheAtlantic: @BioBeef: “CRISPR has been put into So I binge watched many, many crops—nearly all #UnnaturalSelection on Netflix the crop plants that you can last night 'cause that is what transform.” CRISPR groceries animal geneticists with an interest haven't hit shelves yet, but they in #scicomm do on a Friday could be coming soon, night, & have some thoughts @gastropodcast reports: around agricultural applications https://t.co/Z5gmhgdjxf of genome editing so I wrote a BLOG @ucanr @ucdavis https://t.co/pUb0jEPSC7
November 2019 @mtlgazette: @AgBioWorld: The world's banana crops are Whoa! A major breakthrough in under threat from a deadly sorghum, gene editing has fungus. Is gene editing the elevated the protein of this answer? important crop from 9-10% to a https://t.co/7JhFluybow staggering 15-16%. Also https://t.co/gQ2fcyM5s1 improved digestibility. A big deal for Africa & India, another reason to embrace NBT, remove regulatory hurdles https://t.co/2WBlEECemA
December 2019 @singularityhub: @AgBioWorld: #FromtheArchives How to Feed Japan understands that gene- 9.7 Billion People? #CRISPR editing like #CRISPR is not Gene Editing For Crops GMO, just plain old mutagenesis https://t.co/jw4JeerRxC with knowledge & precision! So, https://t.co/ghs5dWpVFR Genome-edited food products to go on sale in Japan, minus scary labelling or unnecessary regulatory burden https://t.co/VpbSW4PkMu
Note: Tweet content is preceded by the posting account (@NAME). Retweets are denoted by RT. Quoted tweets are denoted by QT which indicates a reply to another tweet.
In the future, researchers should consider a longitudinal study to see how frequency of mentions, reach, engagement, and sentiment of the conversation, as well as who participants in it, changes over time with the advancement and prevalence of gene-
47 Texas Tech University, Nellie Hill, August 2020 editing applications in agriculture. Identification of opinion leaders in gene-editing in agriculture should also be of interest to researchers so as to understand who may be driving the innovation-decision process among the public and across subsequent community networks.
To better understand public opinion of gene-editing in agriculture, conversation about the topic could be monitored on other social media platforms, such as Instagram and Facebook, as well as through public opinion polls. A content analysis of the mass media channels on Twitter sharing information regarding gene-editing in agriculture could give insight into what information readers are gathering about the topic.
Although an overview was provided to describe the information garnering peaks and valleys in reach, engagement, and sentiment data, further analysis is needed to investigate the events leading to increased desirable outcomes in those measures. The topics the news media and individual accounts choose to highlight are what make up the conversation. Within that discussion, exploring the uncertain and complex topic of gene- editing in agriculture in 280 characters is limiting. Understanding what types of information beyond the tweet best garners movement through the innovation-decision process would assist practitioners with developing effective explanations of the science to more effectively communicate with the public.
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CHAPTER III
A SYSTEMATIC METAPHOR ANALYSIS OF GENE-EDITING IN AGRICULTURE IN ONLINE UNITED STATES NEWS
Abstract
Although genetic engineering dates back to the 1980s, recent advancements in the field have caused excitement in the scientific community, as well as the public and the press. As the technology continues to evolve, the news media is giving greater attention to DNA-free gene-editing applications in agriculture. Communicating about the complex science of gene-editing often relies on metaphors to help the public understand the technology. The metaphors formed by scientists, science communicators, and journalists are used in news articles when reporting on gene-editing, affecting public opinion. This study sought to identify the metaphorical concepts U.S. news outlets use to describe gene-editing applications in agriculture. The Nexis Uni database was utilized to collected
26 U.S. news media articles published online by four national news media between
January 2, 2015 and June 11, 2019 concerning gene-editing in agriculture. These articles facilitated a qualitative systematic metaphor analysis; that is this approach was used to identify the metaphors used to describe gene-editing in agriculture in terms of process or product. The metaphors were analyzed to develop a list of underlying metaphorical concepts and their frequency of occurrence. The articles conceptualized the topic as creation, a coding program, a fighter, math, targeting, text, and a tool. Overall, the concepts address the complexity, detail, and skill required by gene-editing technologies in agriculture. Future research should investigate how metaphors change over time,
54 Texas Tech University, Nellie Hill, August 2020 region, and outlet. Practitioners are encouraged to utilize accurate metaphors to bridge the scientific community and the public.
Introduction
Although genetic engineering dates back to the 1980s, recent advancements in the field have caused a boom of excitement among the scientific community and the public
(Metje-Sprink et al., 2019; Molteni, 2019a). Modern news coverage largely focuses on
DNA-free Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) biotechnology, a relatively simple and inexpensive method to remove, add, or alter precise, user-selected sections of DNA (Doudna & Charpentier, 2014; Rajendran et al.,
2015). As the technology continues to evolve, the news media give greater attention to the opportunities and consequences of applications in agriculture. A sampling of headlines include “A More Humane Livestock Industry, Brought to You by Crispr” and
“Crispr Can Help Solve Our Looming Food Crisis – Here’s How” featured in WIRED
(Barber, 2019; Molteni, 2019b). The New York Times published “Avocado Toast, Meet
Gene Editing” and “Should Scientists Toy with the Secret to Life?” (The Editorial Board,
2019; Yaffe-Bellany, 2019).
With increased excitement, there is also increased scrutiny of genetic editing technologies. While leaders around the world, including those in science, assess the regulatory requirements necessary to guide the use of such technology in human and agricultural applications, the public is also forming their opinions on the topic (O’Keefe et al., 2015). The pubic is doing so by way of mass media sources, including web-based versions of newspapers and popular magazines (O’Keefe et al., 2015; Schäfer, 2017).
Communicating effectively about the complex science of gene-editing often relies on a
55 Texas Tech University, Nellie Hill, August 2020 wide variety of metaphors to help the public understand the technology (Taylor &
Dewsbury, 2018). The metaphors formed by scientists, science communicators, and journalists are used in news articles when reporting on gene-editing, and therefore, affect public opinion (O’Keefe et al., 2015).
Influence of News Media Coverage of Science
The influence of news media coverage on public opinion is evident (Marks et al.,
2007; Meraz, 2009; Ruan et al., 2019; Schäfer, 2017). How the news media frames biotechnology issues is reflected in the attitudes and beliefs held by the public about such scientific topics (Nisbet et al., 2002; Meraz, 2009). The public is knowledgeable about science, but they are detached from it with little direct experience with many science technologies, including gene-editing (Kennedy & Hefferon, 2019; Schäfer, 2017).
Therefore, the public must rely on news media to gain an understanding of the technology, how it works, and current advancements in applications (Marks et al., 2003;
Marks et al., 2007; McCluskey et al., 2016; Priest, 1994; Schuefele, 2007). Lay citizens and decision-makers alike receive science information, sometimes exclusively, from news media (Schäfer, 2017). More than half of Americans, 68% according to a report from the Pew Research Center, do not seek out science news, but rather just happen across it on the internet (Funk et al., 2017). This means citizens are reading science news from general news sites, not sources specific to science. Another Pew Research Center study found the most common sources Americans receive news is television, followed by news websites, radio, social media, and finally print newspapers (Shearer, 2018).
Mass media coverage remains a connection between the scientific community and the general public (Schäfer, 2012). Even as the public has increasing opportunities to get
56 Texas Tech University, Nellie Hill, August 2020 news from novel information sources, such as social media, they still turn to legacy news media outlets for information (McCombs, 2005; Schäfer, 2017). The prominence and prevalence of legacy news media, such as The New York Times, has given way to a more diverse, less elite landscape of news media outlets (Schäfer, 2017; Ruan, 2019). Whereas people used to happen across science stories on the nightly news or while reading the morning paper, they now are searching the internet for answers from the media
(Dunwoody, 2014). What people are finding is the media increasingly choose to present a greater number of perspectives and arguments when discussing scientific topics. The more diverse landscape of publications reporting on science news means more diverse perspectives, also often characterized by nonscientist sources (Schäfer, 2017). How the media presents scientific issues, such as gene-editing, affects the public’s perception of and trust in science. The public perception created by media coverage affects how gene- editing is discussed among the public, in turn affecting public support, policy, and funding (Bucchi & Trench, 2014; Fischhoff & Scheufele, 2013; Schäfer, 2017).
Given the public will turn to news media to assist in forming their opinion about gene-editing applications in agriculture, there is a need to understand the information currently being presented. Gene-editing technology is complex. Most people will never have direct contact with the technology (Schäfer, 2017). Scientists, science communicators, and science journalists rely on metaphors to communicate such intangible observations and results to the public. Metaphors are the means by which science is accessible to the public because metaphors connect everyday experiences with abstract concepts (Taylor & Dewsbury, 2018). Operating at multiple levels of cognition, metaphors work by activating associations indicating a sense of when, where, who, and
57 Texas Tech University, Nellie Hill, August 2020 how much (Dancygier & Sweetser, 2014). Metaphors direct the attention and cognitive processes of the audience (Moser, 2001). As an example, the development of CRISPR- based technologies has been compared to the development of HTML webpage markup language. This comparison indicates a widely applicable, technological advance can change the world (O’Keefe et. al., 2015).
For all their great potential, metaphors are not without criticism. Executed poorly, metaphors can be imprecise, ambiguous, and misleading. The results can vary from proliferating public misunderstanding of science to exploitation of social and political agendas (Taylor & Dewsbury, 2018). For example, genetic engineering has been explained in terms of “blueprints” and “recipes”, yet been criticized for representing static directions to a tangible product, oversimplifying complex interactions, and lacking a reflection of advancements in the field (Nelkin & Lindee, 2004; Rothman, 2001;
Pigliucci & Boudry, 2011; Rose et al., 2020; Taylor & Dewsbury, 2018). Nevertheless, metaphors hold great promise in helping the public gain an understanding of science
(Taylor & Dewsbury, 2018).
Systematic Metaphor Analysis
Metaphors are pervasive in our natural, daily conversation as well as formal communication in scholarly literature and the popular press. This common component of our language spans speech, text, and sign language across music, dance, architecture, comics, and science (Gibbs, 1994; Lakoff & Johnson, 1980; Forceville, 2008).
“Metaphors are not tools, but rather form a structure in which we live” (Schmitt, 2005, p.
360).
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Metaphorical communication allows the audience to connect two seemly dissimilar ideas by transferring the characteristics of a source domain to a target domain.
Andriessen and Gubbin (2009) gave the example of “the sensorimotor experience of affection as warmth (the warm body of our affectionate mother or father in our childhood) as the source domain when we conceptualize the subjective experience of a relationship (the target domain) as a ‘warm’ relationship” (p. 848).
One method of studying how we use language to create meaning is discourse analysis (Jørgensen & Phillips, 2002), a social constructivist approach to the study of
“language-in-use” or how language is used to “say things, do things, and be things” (Gee,
2010, p. 3). Discourse analysis is an appropriate method for studying how a certain phenomenon or topic is discussed and constructed in language, whereas content analysis would be more appropriate for determining what is being discussed (Schreier, 2012).
Systematic metaphor analysis is a derivative of discourse analysis, as used by Nelson
(2014). Jørgensen and Phillips (2002) identified metaphor as one of the tools for textual analysis of discourse.
In an effort to “reconstruct models of thought, language, and action” (Schmitt,
2005, p. 368), the qualitative research methodology of systematic metaphor analysis, derived from discourse analysis, was developed as an application of Conceptual
Metaphor Theory (Jørgensen & Phillips, 2002). The inductive approach detailed by
Schmitt (2005) and adopted by Andriessen and Gubbins (2009) as well as O’Keefe et al.
(2015) is intended to identify the primary metaphors already being used in text, thus revealing the metaphorical concepts authors utilize to describe a particular phenomenon.
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“Metaphors used in communication are a vital part of being able to understand the sense of things, especially under difficult circumstances” (Schmitt, 2005, p. 368). There is a need to investigate what metaphors are being used by news media to communicate the science of gene-editing applications in agriculture so as to identify the current influences on public opinion regarding the topic (Condit et al., 2002; Maben, 2016;
O’Keefe et al., 2015; Shew et al., 2018; Taylor & Dewsbury, 2018).
Literature Review
Media Coverage of Biotechnology
Media coverage of biotechnology has been studied by a number of researchers, including work focusing on agricultural applications (Marks et al., 2007). Results have varied over time, with studies prior to the late 1990s (Pfund & Hofstadter, 1981; Priest &
Talbert, 1994; Bauer, 2002) primarily reporting more positive frames in news articles, but when public debate rises, the news coverage becomes more negative as it did in the 2000s
(Abbott & Lucht, 2000; Bauer, 2002; Marks et al., 2002; 2003). The shift has been attributed to events that instigated scientific and risk management controversies, such as the bovine spongiform encephalopathy (BSE) food crisis (Marks et al., 2001). Marks et al. (2007) found media coverage about agricultural biotechnology was primarily negative or ambivalent, leading the public to hold the same perspective. The researchers argue insights into news media coverage begets insight into the formation of public opinion over time (Marks et al., 2007).
Ruan et al. (2019) investigated how one major news media outlet in China, the
United States, and the United Kingdom (People’s Daily, The New York Times, and The
Guardian, respectively) portrayed genetically modified organism issues over a span of
60 Texas Tech University, Nellie Hill, August 2020 eight years, 2008 to 2015. The researchers found the news coverage across all three countries predominately focused on either facts, human interest, or conflict and regulation. Coverage in the United States tended to be reported from an industry perspective, focusing on the development of GMOs and highlighting confidence in scientific advancements. Researchers called for future studies to investigate a greater number of media outlets for a more comprehensive view (Ruan et al., 2019).
Marcon et al. (2019) explored how CRISPR was portrayed in context and defined in discussion within U.S. and Canadian popular press news articles between January 2012 and July 2017. Using content analysis, the researchers gathered articles from the top 25 publications based on circulation, online and offline readership, and web traffic. The sample overall positively presented CRISPR with vast potential to change our world, perhaps indicating “inappropriate science hype” (Marcon et al., 2019, p. 2,187). Nearly half (46.9%) of all the articles collected discussed CRISPR in the context of plants or animals. More than 83% of articles also utilized the context of human health to discuss
CRISPR, suggesting articles commonly discussed CRISPR in general terms across a wide variety of applications. A variety of benefits were present in 96.1% of articles, with the benefits of increased food quality appearing in only 2.6% of articles and creating more virus-resistant animals appearing in just 2.2% of articles. Almost all of the articles
(98.7%) described CRISPR as a “gene-editing tool”. Half of the articles went on to explain the functionality of the gene-editing tool in greater detail. The articles repeatedly relied on metaphors such as “(molecular) scissors”, and a “cut-and-paste” or a “search- and-replace” tool like a “word processor” to describe how CRISPR works (Marcon et al.,
2019).
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Media’s Use of Metaphors
Metaphors enable greater economy and efficiency in communication (Ortony,
1975; Richards, 1936; Thibodeau et al., 2019). They are a critical part of language, affecting how we communicate, reason, and understand new, complex, and intangible concepts (Thibodeau et al., 2019). Ubiquitous in the language of science, metaphors are readily utilized by news media to connect the science community with the public
(Marcon et al., 2019; Taylor & Dewsbury, 2018). This is especially true when writing for publications online as “the norm is to write short” (Witschge & Nygren, 2009, p. 47).
Steen et. al. (2010) found 16.4% of words used in news writing are metaphorical.
Researchers have called for continued investigation of the metaphors used to describe genome engineering as the technology evolves and cultural meanings of metaphors evolve as well (Blasimme et al., 2015; Nelson et al., 2016; O’Keefe et al., 2015; Taylor &
Dewsbury, 2018).
Systematic metaphor analysis, originating from discourse analysis, examines how a topic is discussed by identifying primary metaphors in text and constructing the underlying metaphorical concepts utilized by authors to describe a particular phenomenon (Jørgensen & Phillips, 2002; Nelson, 2014; Schmitt, 2005). O’Keefe et al.
(2015) conducted a systematic metaphor analysis of coverage of CRISPR technology in large-circulation newspapers, including The New York Times and the Houston Chronicle, as well as popular science publications, including National Geographic and Scientific
American. The researchers identified a range of metaphors for describing the technology used in human and agricultural applications. The most prevalent metaphor was describing the genome as “text” for editing with CRISPR. Secondly, “targeting” as a means of
62 Texas Tech University, Nellie Hill, August 2020 describing how CRISPR works was a prominent metaphor. Other metaphors included describing the gene-editing technology as a blueprint/construction, code, gambling, map, medicine, origami, as well as a war/battle/fight (O’Keefe et al., 2015). The researchers concluded the metaphors currently in use to describe CRISPR are wide-ranging and largely fall short of describing the technology in important ways. These shortcomings include accurately conveying the operational steps, uncertainty, and potential value of the technology (O’Keefe et al., 2015).
Systematic metaphor analysis has been used to identify metaphors in political news but utilized to a much lesser extent to explore metaphors in science news
(Khudoily, 2018; López-Gonzalez et al., 2018; O’Keefe et al., 2015). The literature does not reveal studies specifically focused on how gene-editing applications specific to agriculture are discussed in the news media. In addition, the development of systematic metaphor analysis as a methodology is still in progress, but with great potential for grasping the language used to communicate science’s most complex issues (Andriessen
& Gubbins, 2009).
Conceptual Framework
Framing and Conceptual Metaphor Theory formed the conceptual framework for this study. A frame is comprised of framing elements, including metaphors, that help convey information to an audience (O’Keefe et al., 2015; Price & Tewksbury, 1997).
Using metaphor to explain complex topics, such as those in the scientific domain, has been shown to influence how people view the issue (Flubserg et al., 2017; Niebert &
Gropengiesser, 2015; O’Keefe et al., 2015; Sopory & Dillard, 2002a; Thibodeau, 2016).
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Framing
Framing is the selection of a portion of perceived reality and making it more salient or prominent in a communication as a means to promote a problem, causal interpretation, moral evaluation or recommendation to the audience (Entman, 1993;
Goffman, 1974). Framing as a theory of media effects suggests how an issue is characterized in news influences how the public understands the issue (Schuefele &
Tewskurg, 2007). News coverage of agricultural biotechnology is cyclical and event driven in nature, where framing has been found to favor new technology on the outset, then disfavor it following biosafety or food safety risk events (Marks &
Kalaitzandonakes, 2001). Marks et al. (2007) later found coverage of such technologies, including biotech foods and genetic testing, was ambivalent or negative when reporting about agricultural applications and more positive when reporting medical applications.
The researchers’ findings support investigating framing in news media to understand the formation of public opinion on issues (Marks et al., 2003).
Framing is intended to convey information in such a way the audience can receive it, relate to it, and interpret it. To accomplish this, the audience members draw on their own schemas to connect the issue with that they know (Price & Tewksbury, 1997).
Therefore, framing is based in cognitive applicability (Schuefele & Tewskurg, 2007). By providing a particular perspective to describe a topic, framing indicates to the audience how to think about a topic (Entman, 1993; 2004). Investigating framing in news media coverage of a topic leads to gaining a clearer understanding of public opinion on the topic. The choices the news media make in their approach to a story from a particular angle affects how the public perceives that story topic (Schuefele & Tewskurg, 2007).
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The frames news media use to present information serve as an organizing or cueing mechanism to help the audience categorize, label, interpret and evaluate the message. Communicators choose framing elements, including words, visuals, and sources, to produce a frame used as a tool to convey information to an audience (Price &
Tewksbury, 1997). Metaphors have been identified as framing tools to represent complex scientific concepts, including gene-editing (Gamson & Modigliani, 1989; O’Keefe et al.,
2015; Pan & Kosicki, 1993; Van Gorp & van der Goot, 2012). Metaphors are not neutral; they highlight or hide certain aspects of a topic (Semino, 2008). The metaphors used by news media influence the subsequent regulatory and ethical schemas used by the audience to interpret the information (O’Keefe et al., 2015). Experimental framing studies have found use of metaphors to influence audience perception of risk, uncertainty, and level of support for solutions to other policy-relevant issues, including climate change and natural disasters (Matlock et al., 2017; Flusberg et al., 2017).
Conceptual Metaphor Theory
Lakoff and Johnson’s (1980) Conceptual Metaphor Theory describes human knowledge as a result of experiences which are tied together to create meaning. This makes human cognition itself metaphorical (Lakoff & Johnson, 1980). Lakoff (1993) defined metaphor as “cross-domain mapping in the conceptual system” and a metaphorical expression as “a linguistic expression (a word, phrase or sentence) that is the surface realization of such a cross-domain mapping” (p. 2). In essence, metaphors state the target domain is the source domain (Lakoff & Johnson, 1980). Put yet another way, a metaphor can be identified by “a linguistic phrase of the form ‘A is B’, such that a comparison is suggested between the two terms leading to a transfer of attributes
65 Texas Tech University, Nellie Hill, August 2020 associated with B to A” (Sopory & Dillard, 2002b, p. 407). The very way DNA-free genome engineering is commonly referred to, which is gene-editing, is itself a metaphor in explaining the science (Kearnes et al., 2018). O’Keefe et al. (2015) found the metaphor of the genome as text was the most common in their review of newspaper and popular science articles on the topic.
Once considered mere flourishes in language, metaphors have more recently been argued to be foundational to the essential connections people must make between one known domain to an unknown domain in order to develop understanding (Lakoff &
Johnson, 1980; Taylor & Dewsbury, 2018). Therefore, metaphors are often used across different communications outlets. Steen et. al. (2010) found approximately 18.5% words in academic texts are metaphorical, 16.4% in news writing, 11.7% in fictional works, and
7.7% in interpersonal conversation. This indicates that non-fiction sources of content (i.e. academic texts and news writing) relied more on metaphors to convey meaning than works of fiction and interpersonal interactions.
Lakoff (1993) argued a significant amount of subject matter, especially topics within science, requires metaphor to be understood. The human perceptual system struggles with interpreting the concepts of solar systems and molecules as well as most other macrocosmic and microcosmic phenomena. Our understanding of these concepts lies in the mesocosm, or “section of the real world we cope with in perceiving and acting, sensually and motorically” (Vollmer, 1984, p. 89). In order to help audiences make sense of science, those communicating about such topics use metaphors to facilitate meaning mapping from everyday experiences to scientific complexities (Niebert & Gropengiesser,
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2015). Audiences use this tool as they naturally like to use metaphors because they make tying together experiences easier (Lakoff & Johnson, 1980).
It is crucial for language used in science to convey “(1) the ethical complexity of the technology; (2) an accurate description of the technology, how it works, and how it can be used; and (3) what is known and unknown about its potential consequences” (O’
Keefe et. al., 2015, p. 4). Yet, previous research has shown metaphors used to describe gene-editing fail by all of these measures (O’Keefe et al., 2015; Pigliucci & Boudry,
2011; Rose et al., 2020). Inaccurate metaphors lead to inaccurate understanding of gene- editing technology by the public as well as policy makers (O’Keefe, et al., 2015).
Nevertheless, metaphors are indispensable in science communication (O’Keefe et al.,
2015; Taylor & Dewsbury, 2018).
News media have the opportunity to use metaphors to create frames to give a particular perspective on the topic of gene-editing in agriculture. The metaphors they choose will shape public opinion on the topic, thus necessitating an investigation of metaphors chosen by news media to describe gene-editing in agriculture (Kövecses,
2018).
Purpose and Research Questions
This study sought to identify and examine how U.S. news outlets use metaphorical concepts to describe gene-editing applications in agriculture in stories published online so as to explore the information the public receives when building their opinion on the topic.
The following research questions guided the study along this purpose:
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RQ1: What metaphorical concepts do online U.S. news articles use to frame gene-
editing applications in agriculture?
RQ2: What are the most prominent metaphorical concepts used in those articles?
Methodology
This study conducted a qualitative systematic metaphor analysis of U.S. news media articles published online concerning gene-editing in agriculture. Relevant articles published between January 2, 2015 and June 11, 2019 were collected using the Nexis Uni database. A data-driven coding process identified the metaphors in the news articles to create, evaluate, and modify a list of underlying metaphorical concepts for final analysis
(Schmitt, 2005).
Systematic metaphor analysis consists of seven steps, outlined by Schmitt (2005), modified by Andriessen and Gubbins (2009), and used by O’Keefe et al. (2015). These steps are:
1. Identify the target topic for metaphor analysis
2. Collect broad background knowledge and metaphors regarding the topic
3. Sample a selection of text on the target topic
4. Identify passages related to the target topic
5. Identify metaphors within passages related to the target topic
6. Group metaphors by underlying metaphorical concept
7. Determine the frequency of each metaphorical concept within the text
Selection of Units
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The first step in systematic metaphor analysis requires identification of the target topic for examination (Schmitt, 2005). For the purpose of this study, the target topic is gene-editing in agriculture.
The second step in the process requires the researcher to examine a wide array of materials so as to gain an overall knowledge of the topic as well as insight into the metaphorical concepts being employed across all types of references. In this study, I conducted extensive collection of materials and information by way of academic journal articles, popular press articles, explanatory videos, and interviews with a scientist specializing in gene-editing in plants.
The third step in the process is to select a sampling of the text regarding the predetermined target topic. Schmitt (2005) lacked specific guidance on this step.
Therefore, this study used the process for discourse analysis, which explains the selection of units for analysis is dependent on the topic, the researcher’s knowledge, and accessibility to content (Jørgensen & Phillips, 2002). This study examined U.S. news media coverage published online regarding gene-editing in agriculture. Online U.S. news articles were selected because countries do not share a common regulatory definition of gene-editing, Americans most often receive news from sources not specific to science, and online news sources are the second most common source of news for Americans
(Funk et al., 2017; Metje-Sprink et. al., 2019; Shearer, 2018).
Content was accessible through news article databases. In Newsbank and Nexi
Uni databases, a search was conducted to identify online U.S. news articles pertaining to gene-editing in agriculture between 2015 and 2019. The time frame for the search was set for articles published by news media in the United States. The search window was
69 Texas Tech University, Nellie Hill, August 2020 selected as a preliminary search revealed no articles within the search terms were published before 2015. The collection timeframe ended in 2019 so as to acquire a manageable sample that still covered several years of technological advancements in gene-editing. The keywords included in the search were based on the purpose of the study as well as the in-depth collection of literature and terminology used to discuss gene-editing. This review led to a search for the root keywords of “gene-editing”, “gene editing”, and “genome editing”. Each root keyword also required at least one of the following keywords: “agriculture”, “animal”, “livestock”, “crop”, “food”, and “plants”.
The search excluded articles with the keywords “baby” and “babies”. The complete
Boolean search was: “gene-editing” or "gene editing" or "genome editing" and livestock or crop* or animal* or plant* or food* and not baby and not babies. The asterisks indicate a wildcard search for the term. The search returns content in an alternative form of the keyword. For example, “plant*” also returns articles with “plants” and “planting” if the text in the article matches the rest of the search requirements.
Specific guidance on the number of texts required by discourse analysis or systematic metaphor analysis is sparse, quantified by neither Jørgensen and Phillips
(2002) or Schmitt (2005). Jørgensen and Phillips (2002) stated, “It is often sufficient to use a sample of just a few texts. The reasons for this are that discursive patterns can be created and maintained by just a few people” (p. 120). Nevertheless, this study is guided by O’Keefe et al. (2015) who analyzed 22 texts from seven newspapers and 24 texts from three popular science publications. In addition, Nerlick and Hellsten (2009) analyzed 22 articles about microbiomics from 19 news sources. Andriessen and Gubbins (2009) analyzed three seminal journal articles to study social capitol theory. The selection of
70 Texas Tech University, Nellie Hill, August 2020 units for their study was based on this research and the guidance of Jørgensen and
Phillips (2002).
To address the purpose of this study, publications were limited to national news sources available online. Based on the Nexi Uni search results, The New York Times,
Washington Post, and The Atlantic were chosen to represent national news media as these
U.S. news publications with national readership who published the most articles about gene-editing in agriculture during the time period of the study. The Associated Press was included as Newsbank search results indicated many state news outlets across the country picked up stories from this wire service. All the articles included in the analysis were published online and collected via Nexi Uni. Articles returned based on the search criteria were included in systematic metaphor analysis if they primarily described gene-editing in agriculture.
Based upon all the aforementioned criteria, 26 unique articles pertaining to the study from four news media sources were identified. The New York Times published 13 articles, The Associated Press published seven articles, the Washington Post published four articles, and The Atlantic published three articles.
Unit Analysis
The fourth step in the process of systematic metaphor analysis is identification of passages related to the target area. In this step, the body text within the selected articles was read for passages that described gene-editing in agriculture in terms of process or product. These passages were highlighted, then further analyzed in step five of the process, identification of metaphors within the passages related to the target topic.
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Influenced by Lakoff and Johnson (1980), Schmitt’s (2005) provided the three-part identification formula used to assess the passages for metaphors:
a. “a word or phrase, strictly speaking, can be understood beyond the literal
meaning in the context; and
b. the literal meaning stems from an area of sensoric or cultural experience
(source area),
c. which, however, is transferred to a second, often abstract, area (target area)” (p.
371).
Once metaphors are identified, step six in the process of systematic metaphor analysis calls for grouping metaphors by underlying metaphorical concept (Schmitt,
2005). Lakoff and Johnson (1980) asserted metaphors do not happen by chance, they can always be traced back to a small number of common concepts. Schmitt (2005) cautioned against over-interpretation of the text as striving to discover a root metaphorical concept oversimplifies discourse on a given topic. Following the recommendations of Schmitt
(2005), this study utilized a creative, synthesizing approach to sort metaphors by those
“belonging to the same image source and describing the same target area are grouped into metaphorical concepts” (p. 373). All metaphors identified in the text were grouped by metaphorical concept in this fashion.
The process of systematic metaphor analysis concludes with determining the frequency of each metaphorical concept within the text. This is an important step, even in this qualitative study, as the number of instances of a metaphorical concept is an indication of how prolific the concept is in the discourse of the topic (Schmitt, 2005). To begin data analysis, all of the highlighted metaphorical passages were copied over to a
72 Texas Tech University, Nellie Hill, August 2020 new document. Then, the frequency of each source domain (keyword) within a metaphorical concept was tallied and summed for a total frequency of each concept.
Dependability and Credibility
Schmitt (2005) situated systematic metaphor analysis within the standards of rigor assigned to qualitative research by Steinke (1999). Schmitt pointed out several “steps to trustworthiness” (p. 380) that fall within two primary standards of rigor in research – dependability and credibility.
Dependability in qualitative research refers to the trustworthiness or consistency of the study (Ary et al., 2010). The researcher must seek to demonstrate consistent methods that can be reproduced and utilize an appropriate approach and procedures for the context. External evidence can be used to test conclusions (Ary et al., 2010). In order to enhance dependability, the researcher kept an audit trail with details regarding decisions made about the study, what was carried out, when, and why (Lincoln & Guba,
1985; Schmitt, 2005). Furthermore, a peer review process was established to support the dependability, consistency, and quality in the identification of metaphors and the construction of underlying metaphorical concepts. The peer review began with providing background gene-editing in agriculture literature to one peer reviewer, a master’s student studying agricultural communications at a Southern institution. I then randomly selected one article from each of the four news sources. The peer reviewer and I independently identified within the articles’ passages related to the target topic of gene-editing in agriculture. Subsequently, we independently identified the metaphors within the passages related to target topic. Upon completion, the peer reviewer and I discussed our coding and rectified differences where necessary. I used these four articles to guide analysis of
73 Texas Tech University, Nellie Hill, August 2020 the remaining articles in the sample then grouped the metaphors by underlying metaphorical concept. Next, I randomly selected a set of news articles, one article from each of the four news sources, for the initial peer reviewer and another set of news articles for a second peer reviewer, a professor in agricultural communications at a
Southern institution, to verify coding. Peer reviewers brought their own academic, agricultural, and scholarly knowledge and experience to the peer review process. The research team discussed and rectified inconsistencies (Creswell, 2007; Lincoln & Guba,
1985; Merriam & Tisdell, 2015; Schmitt, 2005). In total, 10 of the 26 articles were included in the peer review process. A week later, I reviewed and discussed the metaphorical concepts with a committee of researchers in agricultural communications as well as plant and soil science, which initiated another researcher review of the articles and adjustment of the concepts. Finally, a conclusive round of adjustment to the metaphorical concepts was conducted and verified with the second peer reviewer.
Credibility in qualitative research refers to confidence in research observations, interpretations and conclusions (Art et al., 2010). In order to enhance credibility, the researcher established evidence of controlling bias by using reflexivity, or self-reflection, by journaling about the methodological process and personal bias throughout the research process. The peer review process also built credibility by establishing consensus of interpretation when beginning to identify metaphors in the news articles, and then again after analysis was complete. Referential adequacy further supported the credibility of the study by utilizing low-inference descriptors, that is the exact terms used within the selected news articles, to create the underlying metaphorical concepts. All interpretations of the text always referred back to the material (Ary et al., 2010; Schmitt, 2005).
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Results
RQ1: What metaphorical concepts do online U.S. news articles use to frame gene- editing applications in agriculture?
Systematic metaphor analysis of 26 online U.S. news articles from four sources revealed seven metaphorical concepts used to describe gene-editing application in agriculture. These seven concepts are the product of grouping individual metaphors, identified by source domain keywords. These metaphorical concepts connect two seemingly dissimilar ideas by transferring the characteristics of a source domain
(keywords) to a target domain (gene-editing is…) (Andriessen & Gubbin, 2009; Schmitt,
2005). Table 3.1 presents each of the identified metaphorical concepts in alphabetical order accompanied by the source domains within each concept and U.S. news media sources in which the concepts were found.
Table 3.1
Metaphorical Concepts Describing Gene-Editing Applications in Agriculture
Metaphorical Source Presence in U.S. Concept Domain News Media Sources Creation Design, reshape, seeds of change, NYT, WP, AP revolution, playing God, precision breeding, rewilding, stream, playing, franken-, science fiction, menagerie, flood
Coding program Software, agile programming, big data, NYT, WP styles, pre-coded, on demand
Fighter Knocked out, fight NYT
Math Add, subtract AP, NYT
Target Target, off-target NYT, WP, AT, AP
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Metaphorical Source Presence in U.S. Concept Domain News Media Sources Text editor Edit, copy, cut, paste, find-replace function, NYT, WP, AT, AP delete, template, search and replace function, rewrite, alphabet, word processor
Tool Surgically, swap, meld, repair machinery, NYT, WP, AT, AP cut, tool, work, build, snip, tweak, switch, on/off, slice, fix, scissors, tinker, glitch, toolkit, prop up
Note. U.S. news media sources are abbreviated as follows: The New York Times, NYT; The Washington Post, WP; Associated Press, AP; The Atlantic, AT
The metaphorical concept of gene-editing as creation has dual uses. The concept is used to emphasize the positive progress gene-editing allows as well as stress the moral challenges of gene-editing. In terms of positive progress, the concept likens the target domain, gene-editing, to customizing, designing, and otherwise making new plants and animals. Gene-edited plants and animals are described as being a product of a design process, as Harmon (2015) explained, “With funds from the United States Department of
Agriculture, Bhanu Telugu, a University of Maryland researcher, is trying to design pigs so they can no longer serve as a reservoir for the flu virus” (para. 22). Sometimes gene- editing is explained as a way to revive old strains of genetics. One article in particular tried coining gene-editing as “rewilding” stating, “But what if scientists used the precise techniques of today's molecular biology to give back to plants genes that had long ago been bred out of them? And what if that process were called ‘rewilding’” (Kolata, 2015, para. 2).
In terms of moral challenges, the metaphorical concept of creation likens the target domain to Biblical references as well as out-of-control science in need of oversight.
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Examples of such references include, “Beyond worries about ‘playing God,’ it may be an uncomfortable reminder of how modern food production already treats animals, said Paul
Thompson, a professor of agriculture at Michigan State University” (Choi, 2018, para.
15). Products in gene-editing are explained both as being and not being “Franken-” plants and animals. On the side of being, Feltman (2016) wrote, “His frankenfungi is an
Agaricus bisporus, the kind of white-button mushroom that can be bought at any grocery store” (para. 6). On the side of not being, Chang (2017) quoted her source, “‘This is not
Frankenfood,’ said André Choulika, chief executive of Cellectis, one of the companies developing gene-edited crops” (para. 6). This difference led to Harmon (2015) likening gene-edited plants and animals to those in a controlled, zoo-like environment in stating,
“A menagerie of gene-edited animals is already being raised on farms and in laboratories around the world” (para. 5).
The metaphorical concept of gene-editing as a coding program likens the target domain to software that can be agile, recoded, and tailored with style sheets much like computer code. For example, Hardy (2015) stated:
Most strikingly, the way they propose to create their bio-based “software”
parallels recent changes in the way computer software is written. Instead of grand,
complex projects, they are targeting little changes at a fast rate, and adjusting as
clever analysis yields more information — a concept high-tech firms call agile
programming. (para. 5)
Hardy (2015) went on to expand the metaphor by way of a source, “‘We have better tools, better computational biology,’ said Markus Pompejus who runs a biotechnology program for BASF, the German chemical giant” (para. 24). Harmon
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(2015) extended the concept to the ready accessibility of making changes in stating,
“They allow scientists to remove and replace bits of genetic code more or less on demand” (para. 9).
The metaphorical concept of gene-editing as a fighter likens the target domain to being capable of a knock-out in a fight. For example, Kolata (2015) quoted source, Nina
Fedoroff, a plant researcher and emerita professor at Pennsylvania State University, in stating, “They knocked out a gene that makes a plant susceptible to rust” (para. 24). In addition, Harmon (2015) described why gene-editing in plants and animals is carried out by stating, “some to fight disease” (para. 5).
The metaphorical concept of gene-editing as math is presented as a simple calculation. Choi (2018) explained how one entity uses gene-editing in stating, “A company wants to alter farm animals by adding and subtracting genetic traits in a lab”
(para. 2).
The metaphorical concept of gene-editing as targeting has dual use, presenting the exactness of the technology or cautioning unintended consequences. With regards to exactness, Feltman (2016) stated, “But Yang targeted several genes that code for the protein that causes mushrooms to turn brown as they age or get bruised” (para. 6). With regards to caution, Neergaard (2018) wrote, “The biggest concern is what are called off- target edits, unintended changes to DNA that could affect a crop's nutritional value or an animal's health, said Jennifer Kuzma of the Genetic Engineering and Society Center at
North Carolina State University” (para. 23).
The metaphorical concept of gene-editing as a text editor likens gene-editing to writing, rewriting, and moving words in a word processor. This concept is exemplified in
78 Texas Tech University, Nellie Hill, August 2020 the articles by such an example as, “Companies like Calyxt have portrayed gene-editing more like moving the cursor in a word processor to a particular location and making a small change to the text” (Chang, 2017, para. 12). In addition, this metaphor likens gene- editing to be like a shortcut to making changes to genetic code. Hardy (2016) explained gene-editing as, “This capability, commonly spoken of as the genetic version of cutting and pasting in a word-processing program, bypasses the slow adjustments to a complex ecosystem that happen when nature brings forth a new species” (para. 14).
The source domains within the metaphorical concept of gene-editing as a tool liken gene-editing to a material, mechanical instrument able to be welded to repair, fix, or adjust the genetic code of organisms. Examples of such references include, “With the ability to cut DNA at a specific site, they can let the cell’s DNA repair machinery paste in new sequences, usually a gene of interest, in the process of annealing the two cut ends of the DNA molecule” (Wade, 2015, para. 20). Dewey (2018) explained how scientists use the gene-editing as a tool in stating, “Scientists can now turn plant genes ‘on’ and ‘off’ almost as easily as Calyxt scientists flip a switch to illuminate the rows of tender soybean plants growing in their lab” (para. 3). Gene-editing is also linked to attempts to repair genes as Choi (2019) put it, “The move comes as companies are turning to newer genetic engineering techniques that make it easier to tinker with the traits of plants and animals”
(para. 2).
RQ2: What are the most prominent metaphorical concepts used in those articles?
The final step of Schmitt’s (2005) systematic metaphor analysis is determining the frequency of each metaphorical concept within the text. This step allows for a ranking of the metaphorical concepts in terms of which are most frequently used in the news
79 Texas Tech University, Nellie Hill, August 2020 articles. As an example, to determine the frequency of the metaphorical concept of “gene- editing is math” all instances within the highlighted metaphorical passages of adding and subtracting were counted. Three instances of this concept were found. For the purpose of this study, references to “gene-editing” were not counted as this is the common name of the technology and including this term in the count would skew results. Figure 3.1 presents each of the identified metaphorical concepts and their prominence relative to each other.
Figure 3.1
Prominence of Each Gene-Editing Metaphorical Concept
55 53
28 Frequency 8 9 2 3
Creation Coding Fighter Math Target Text editor Tool program Metaphorical Concept
The most frequently used metaphorical concept found in the news articles was the concept of gene-editing as a tool. A count of the source domains within this concept appearing in the news articles yielded 55 instances. The concept of gene-editing as a text editor appeared 53 times in the articles. Creation was the third most frequently used 80 Texas Tech University, Nellie Hill, August 2020 concept, appearing 28 times in the units analyzed. In the distant fourth rank, gene-editing as targeting appeared nine times in the articles. Gene-editing as a coding program appeared eight times, as math appeared three times, and as a fighter appeared twice.
Discussion, Conclusions & Recommendations
Metaphors are a bridge scientists and news media use to connect the science community with the public by relating the known with the unknown, particularly when discussing often complex topics within science (Lakoff & Johnson, 1980; Taylor &
Dewsbury, 2018; Witschge & Nygren, 2009). Public opinion is influenced by the metaphorical concepts news media utilize to explain the science of gene-editing applications in agriculture (Condit et al., 2002; Maben, 2016; O’Keefe et al., 2015; Shew et al., 2018; Taylor & Dewsbury, 2018). The purpose of this study was to identify the metaphorical concepts related to gene-editing in agriculture used by U.S. media in online news articles.
Previous literature has described genomes, gene-editing, and CRISPR as code to be translated, blueprints for building organisms, and battles to be won among other metaphors (Condit, 1999; O’Keefe et al., 2015). This study, which looked exclusively at gene-editing in agriculture in online U.S. news, identified some of these same metaphorical concepts as well as new ones. Results indicated seven metaphorical concepts proliferated the online U.S. news articles analyzed, conceptualizing gene-editing in agriculture as creation, a coding program, a fighter, math, targeting, a text editor, and a tool. These metaphors are not neutral (Semino, 2008). They each provide a lens through which an audience can view gene-editing in order to evaluate it using their own schemas
(Entman, 1993; Goffman, 1974; Price & Tewksbury, 1997).
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Even as these metaphorical concepts offer different perspectives on gene-editing, they are also sometimes found together in the articles analyzed. For example, Harmon
(2015) wrote “a menagerie of gene-edited animals is already being raised on farms and in laboratories around the world – some designed for food, some to fight disease, some, perhaps, as pets” (para. 5). Here the concepts of creation and fighting are mixed, which requires two different connections between what the audience knows and what they unfamiliar with, gene-editing. Additional research is needed to explore differences in understanding or elaboration by the audience depending on if singular metaphors or mixed metaphors are used to explain a complex topic, such as gene-editing.
Taken separately, each metaphorical concept draws on different everyday experiences to map meaning onto the scientific complexity of gene-editing (Niebert &
Gropengiesser, 2015). In addition, each concept contributes to the larger body of language used to communicate science to the public. O’Keefe et al. (2015) called for this language to accurately convey the description, complexity, and possibilities of a technology such as gene-editing.
The third most common metaphorical concept in this study, gene-editing as creation, has not been introduced in previous literature. This may be due to the agricultural nature of this study, which should be further investigated in future research.
The duality of the creation concept in terms of positive progress or a moral challenge allows for the exploration of the complexity of gene-editing in agriculture. We must advance plant and animal production to meet the needs of a growing population, but we must also grapple with the ethical and moral implications of doing so. Depending on which use is employed and the stance of the message receiver, this metaphor could
82 Texas Tech University, Nellie Hill, August 2020 trigger an unbalanced, gut reaction to the topic. Nevertheless, the concept of creation does convey development and novelty, which are characteristics of gene-editing technology.
A significantly smaller portion of the metaphors in the articles related to the concept of gene-editing as a coding program (O’Keefe et al., 2015). This concept may be a derivative of genetic code being altered by modern genome engineering technology.
Coding is complex and requires great attention to detail. If not written perfectly, a coding program will not work. Yet, code has limitless possibilities in developing programs with a wide variety of applications. These characteristics are shared with gene-editing technology. However, there may be audiences without such understanding of a coding program in order to draw these inferences over to gene-editing.
The concept of gene-editing as a fighter has appeared in previous research more so as a war or battle to be won (O’Keefe et al., 2015). In this study, the metaphor conveys aggression in terms of a simple knock-out as well as a long-term fight. This metaphor leaves much to the imagination as to the complexity of getting into the ring. It also lacks detail as to how throwing the punches of gene-editing works. Winning a fight is the end, leaving no room for future possibilities or advancements.
Gene-editing as math is found minimally in the articles included in this study but may be more useful if it were expanded to include higher-level math. The articles commonly used simple arithmetic, adding and subtracting, to describe gene-editing.
However, math can be a very complex process when we begin to describe areas such as algebra or calculus. A math equation can be altered as a part of the process of getting the right answer, much like that altering genetic code to secure the desired outcome.
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O’Keefe et al. (2015) found gene-editing as targeting to be the second most used metaphorical concept in their analysis of news regarding use of CRISPR in general. In this study, the concept of targeting was a distant fourth. The difference may be due to the different timeframes of data collection, indicating an evolution of metaphors used to describe gene-editing, but further investigation is needed. The dual use of targeting, gene- editing as being on target or off-target, indicates precision of the technology as well as the dangers when the target is missed. The image of a target conjures assumptions of a dangerous projectile used to hit it, implying gene-editing technology must be handled with care.
Gene-editing as a text editor was the second most commonly found metaphorical concept in this study, and the second most common found by O’Keefe et al. (2015). The concept draws meaning from writing, rewriting, and moving words in a word processor to improvements made by gene-editing. While this metaphor is likely to draw upon knowledge held by many audiences, it has been criticized for only implying positive results as editing text always suggests improvement (O’Keefe et al., 2015). Gene-editing as a text editor does allow for connections between the makeup and complexity of language and the makeup and complexity of genetic code. Just as writing is a process, so is the discovery of new methods and applications of gene-editing.
The most frequently used metaphorical concept found in this study was that of gene-editing as a tool. While O’Keefe et al. (2015) identified this concept using the term
“mechanism”, it was not the most frequently used in their analyzed articles. The tool metaphor is used to convey the action of how gene-editing works, while also signaling the many different means by which the alteration of genetic code takes place. Different
84 Texas Tech University, Nellie Hill, August 2020 tools may do different tasks, just as gene-editing may work in different ways to alter the genome in various plants and animals. There are many tools that must be used with care and attention to the task at hand. The same can be said for the use and process of gene- editing technology.
Future research should investigate the effectiveness of each metaphorical concept in terms of causing consideration and thought regarding the topic. O’Keefe et al. (2015) called for the description of complex science, particularly in the language of metaphor, to be honest about the development and uses of technology. The authors specifically called for metaphors that lead the audience to engage in “thoughtful deliberation” (O’Keefe et al., 2015, p. 8). The results of this study identified common metaphorical concepts being used in the popular press, so future research should strengthen and test these concepts to see which are the most effective in causing elaboration, or meaningful thought, regarding the topic of gene-editing in agriculture.
There are more instances of gene-editing in agriculture being discussed in online
U.S. news than was included in this study; however, these discussions are intertwined with use of the technology in humans. The interaction of agricultural and human gene- editing may be of concern as human applications of the technology bring up additional moral and ethical concerns among the public (Baumann, 2016). Future research should investigate the implications of discussing gene-editing in human and agricultural contexts together to see if this affects acceptance of plant and animal products of the technology.
Practitioners may need to bolster advancements in gene-editing through communications that clearly differentiate agricultural applications from human ones, and even neglect to mention human applications in such messaging.
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As Schmitt (2005) pointed out, “The ‘richer’ in knowledge the researcher is, the
‘richer’ will be the links that can be produced” (p. 378). Therefore, inferences of the metaphors may be interpreted further or differently by another researcher. This is the nature of systematic metaphor analysis (Schmitt, 2005). Nevertheless, prior to beginning the study, I followed Schmitt’s (2005) recommendations of socializing in the language and environment of the topic so as to develop an understanding of gene-editing in agriculture in terms of how it works, how it is being discussed, and the agricultural products of the technology.
The method of systematic metaphor analysis does not take into consideration the bias introduced by one publication’s prolific use of a metaphor while another publication may use it significantly less (Schmitt, 2005). There is greater nuance within the findings of frequency of each metaphorical concept that warrants additional analysis in this study, as well as attention by future researchers who utilize this method.
The news articles included in this study represented only online news from four outlets that are all national publications, but all are based on the east coast of the U.S.
These outlets were the undisputed prominent publishers of content pertinent to the study.
Nevertheless, further research should investigate if other news outlets, particularly across regions and specialty, utilize different metaphors when reporting on gene-editing in agriculture. Regional differences in how the audience receives, relates, and interprets metaphors in gene-editing may be present, affecting how people in different regions of the United States understand gene-editing in agriculture (Entman, 1993; Goffman, 1974;
Lakoff & Johnson, 1980; Price & Tewksbury, 1997). As metaphors differ across regions, they may also differ over time. In addition, future research should investigate if there is a
86 Texas Tech University, Nellie Hill, August 2020 difference between how agricultural publications and mainstream media use metaphors to connect with their audiences. Longitudinal research should investigate how the metaphors used by mass media change over time as the technology develops and public understanding and opinion evolves.
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CHAPTER IV
PERSUASIVE EFFECTS OF METAPHORS REGARDING GENE-EDITING IN AGRICULTURE
Abstract
The future of agriculture is closely tied to agricultural products of gene-editing.
Given the important role of gene-editing in our food system, exploring opportunities to persuade public acceptance of the technology is critical. Metaphors are an opportunity to influence public acceptance because metaphors encourage issue-relevant thinking and enhance persuasion. The purpose of this study was to determine the persuasive effects of metaphorical concepts regarding gene-editing in agriculture. A between-subjects, experimental research design was used to investigate which metaphorical concept for gene-editing in agriculture causes the most issue-relevant thinking and willingness to share on social media. The metaphors of gene-editing as creation, a text editor, and tool were embedded into a mock news article. A control mock news article was also created.
Three hundred participants provided demographic information, indicated deference to scientific authority, and shared their perceived and factual knowledge of gene-editing in agriculture. After viewing the randomly assigned mock news article, participants shared thoughts they had while reading the article, then indicated their willingness to share the article on social media. Finally, participants responded to items regarding their coronavirus outbreak experience and need for cognition. Even when controlling for confounding variables, the results indicated no significant differences between the treatments on issue-relevant thinking or willingness to share the article on social media.
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Future research should explore the impact of metaphorical concept on attitude as well as other behavioral outcomes associated with elaboration.
Introduction
Genetic improvement revolutionized agriculture in the 1950s and 1960s. Led by
Norman Borlaug, the Green Revolution is marked by adoption of new technologies including high-yielding varieties of wheat and rice crops that enabled incredible increases in food production across the globe. Almost all the wheat we eat today is dwarf wheat, cultivated by Borlaug and his colleagues to allow farmers to grow more grains to feed the hungry (Ortiz et al., 2007; Vegauwen & De Smet, 2017).
In 1944, Borlaug became head of the wheat research program for the Office of
Social Studies, a cooperative project between the Rockefeller Foundation and the
Mexican Ministry of Agriculture (Ortiz et al., 2007). The goal was to improve wheat crops. The primary responsibility was integrating research information into technology and disseminating it to farmers. The first semi-dwarf wheat cultivars were released in
Mexico in 1962 (Ortiz et al., 2007). The resulting increase in food production in Mexico,
India, Pakistan, and Asia saved millions of lives. Borlaug was awarded the Nobel Peace
Prize in 1970 for his work in ground-breaking wheat improvement research and his tireless dedication to deliver such improvements to farmers (Ortiz et al., 2007).
Traditional breeding of a new plant cultivar ready for release, such as wheat or rice, takes 10 to 12 years, consisting of hybridization, line fixation, and field trial stages.
Shuttle breeding, popularized by Borlaug, halves this time by using different field locations or nurseries to allow for off-season breeding in contrasting conditions (Lenaerts et al., 2019; Ortiz et al., 2007). The development of modern gene-editing technology,
97 Texas Tech University, Nellie Hill, August 2020 which mainly uses TALENS and CRISPR-systems, accelerates this timeline even further alongside greater efficiency, accuracy, and ease of use (Llewellyn, 2018; Metje-Sprink et al., 2019; Molteni, 2019).
Gene-editing is a group of technologies that gives scientists the ability to change a plant or animal’s DNA. These technologies allow genetic material to be added, removed, or altered at particular locations in the genome without the introduction of foreign genes into the organism (National Institutes of Health, 2020). The technology has been heralded as a critical innovation to meet the most pressing challenges facing food production: population growth and climate change (Lenaerts et al., 2019; Shew et al., 2018). The numerous applications of gene-editing in plants and animals include disease control, pest eradication, speed breeding, nutritional and quality enhancements, extreme weather resistance, and deletion of allergies (Doudna & Charpentier, 2014; Duran, 2016; Goold et al., 2018; Rajendran et al., 2015; Regalado, 2015; Shrock & Güell, 2017). Gene-editing allows for novel improvements to plants and animals so they can come into production more quickly than using traditional breeding to achieve the same results (Llewellyn,
2018).
Accelerated improvement of plants and animals is critical if food production is to keep pace with world population growth. It is predicted that global population will reach
9.8 billion people by 2050, and food production is currently not keeping pace with population growth. According to FAO et al. (2019), more than 820 million people do not have enough to eat and hunger is steadily increasing. In 2018, 37 million people in the
United States were food insecure (Coleman-Jensen et al., 2019). The outbreak of the novel coronavirus, COVID-19, in 2020 is projected to increase this number by 3.3 to 17.1
98 Texas Tech University, Nellie Hill, August 2020 million people, depending on changes to unemployment and poverty (Feeding America,
2020). While attention should be given to more equitable food distribution and significant reduction of food waste globally, these measures will not fill the expected gap in food production needed now or by 2050 (Llewellyn, 2018).
For scientists to develop safe and efficient solutions to food scarcity with the additional pressures of a growing population and changing environment, they must have continuous communication with the public so as to positively influence acceptance of such solutions (Georges & Ray, 2016). This communication must promote public understanding of risks, benefits, goals, and means of science so as to combat misinformation (Georges & Ray, 2016). Public discussion of the merits of gene-editing applications in agriculture has only just begun. As products get closer to retail shelves, it is important for the public to have accessible information regarding the implications of the technology (Brossard, 2018).
The public often turns to mass media sources such as web-based versions of newspapers and popular magazines to form their opinions of agricultural biotechnology, including gene-editing (O’Keefe et al., 2015; Schäfer, 2017). Although people are knowledgeable about science, they have little direct experience with it (Kennedy &
Hefferon, 2019; Schäfer, 2017). To gain an understanding of gene-editing technology, how it works, and current advancements in applications, lay citizens and decision-makers receive science information, sometimes exclusively, from news media (Marks et al.,
2003; Marks et al., 2007; McCluskey et al., 2016; Priest, 1994; Schuefele, 2007).
The most common sources from which Americans receive news is television followed by news websites, radio, social media, and finally print newspapers (Shearer,
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2018). Most often, people are exposed to science news not from sources specific to science, such as Scientific American, but instead from happening across it in their normal news consumption on the internet, such as reading articles from The New York Times
(Funk et al., 2017). Online news articles also appear on social media platforms. More than a third of Americans report social media is an important means to get science news
(Funk et al., 2017). Even as the public has increasing opportunities to get news from novel information sources, such as social media, they still turn to legacy news media outlets for information (McCombs, 2005; Schäfer, 2017). The perception of gene-editing technology created through media coverage affects how the topic is discussed among the public, in turn affecting public support, policy, and funding (Bucchi & Trench, 2014;
Fischhoff & Scheufele, 2013; Perrault & O’Keefe, 2019; Schäfer, 2017).
Mass media coverage of gene-editing links the scientific community with the general public to allow for the continuous flow of information needed to influence public acceptance of the technology (Georges & Ray, 2016; Schäfer, 2012). Recent advancements in gene-editing technology, particularly around CRISPR systems, have caused a boom in media coverage. Scientists, science communicators, and journalists utilize metaphors to explain gene-editing in a manner that connects the everyday experiences of the audience with the complex and abstract science of gene-editing
(O’Keefe et al., 2015; Taylor & Dewsbury, 2018). Metaphors transfer the characteristics, relations, and operation of one familiar domain to an unfamiliar domain (Gentner, 1982).
Metaphors present the novel in terms of the known. For example, Hill (2020) found gene- editing in agriculture described in the popular press as a word processor able to edit text.
The known domain is text in a word processor; the unknown domain is gene-editing. The
100 Texas Tech University, Nellie Hill, August 2020 receiver of the metaphor draws connections between how they can edit words in a software program and how scientists edit the genetic sequence of DNA through gene- editing (Hill, 2020).
On one hand, metaphors give nonexperts insight into science by making sense of otherwise foreign ideas, concepts, and knowledge. On the other, metaphors can be used to spread misinformation and misunderstanding while obstructing scientific study (Taylor
& Dewsbury, 2018). Despite this, metaphors are critical, indispensable messengers between the scientific community and public understanding of science (Taylor &
Dewsbury, 2018). As such, metaphors “are not just decorative rhetorical devices that make speech pretty but are fundamental tools for thinking about the world and acting on the world” (McLeod & Nerlich 2017, p. 1). As such an important part of language, researchers call for “empirically-grounded research in critical discussions of metaphor use in the life sciences” (Taylor & Dewsbury, 2018, p. 3).
The future of agriculture is closely tied to agricultural products of gene-editing
(Rose et al., 2020). Given the important role of gene-editing in our food system, exploring opportunities to persuade public acceptance of gene-editing technology is critical. Metaphors stand as an opportunity for science to positively influence public acceptance of gene-editing because metaphors activate multiple levels of cognition, encourage issue-relevant thinking, and enhance persuasion (Dancygier & Sweetser, 2014;
O’Keefe, 2016; Sopory & Dillard, 2002). There is a need to investigate what metaphors for gene-editing in agriculture are most successful in reaching these desired outcomes
(Hill, 2020; O’Keefe et al., 2015; Taylor & Dewsbury, 2018).
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Literature Review
Public Opinion of Gene-Editing
Breeding and selecting for desirable traits in plants and animals is not new to agriculture, but modern gene-editing tools accelerate this process. Therefore, with this advancement comes the dawning of the next generation of genetically engineering crops
(Rose et al., 2019). Scientists continue to find new ways to utilize gene-editing technology, which leads to greater prominence in discussion and debate (Molteni, 2019).
While there is enthusiasm for the potential of gene-editing technology, there is also concern. Societal, cultural, and ethical conversations surround the potential impacts of the technology (Brossard, 2018). Those conversations include public uncertainty regarding how genetically modified food and food enhanced by gene-editing differ, as well as a lack of knowledge about how gene-editing works (Ishii & Araki, 2016; Lusk et al., 2018;
McFadden & Lusk, 2016; Miller, 2004; Rainie, 2017; Zerbe, 2004).
Public opinion of gene-editing in the United States has been studied by way of analysis of public opinion polls, surveys, as well as social media monitoring. Yabar et al.
(2018) used three different research methods to investigate U.S. public opinion of genetic engineering: opinion poll analysis, media discourse analysis, and Twitter discourse analysis. The researchers conducted an aggregate analysis of public opinion polls that asked questions about CRISPR technologies, gene therapy, and genetic engineering.
Results indicated declining support for genetic modification over time, though poll questions with positive wording elicited more positive results. When looking at the demographics of poll respondents, the absolute value of support within a demographic group varied from poll to poll, but the range of approval was consistent (Yabar et al.,
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2018). The media discourse analysis collected articles regarding CRISPR from six online news media sources and revealed the articles commonly use umbrella terms, such as gene-editing, in the headline instead of specifically mentioning a gene-editing technology, such as CRISPR. About a third (35%) of the articles gave a passable explanation of what CRISPR does, while 39% of articles gave a vague explanation, and
26% gave no explanation at all (Yabar et al., 2018). The Twitter discourse analysis found more than half of all the tweets (65%) were shared directly from news articles, blogs, and scientific journals. No shifts in tone or content of tweets was found over the study’s timeframe, though 91% of tweets were neutral in tone (Yabar et al., 2018).
Hill (2020) conducted a descriptive social media analysis of Twitter content related to applications of gene-editing in agriculture. Results indicated the amount of conversation and the number of contributors to it was relatively stable over the time of interest to the study. Engagement with the conversation increased over time. In addition, positive sentiment of the tweets discussing gene-editing in agriculture trended up over the study’s timeframe (Hill, 2020).
In a survey of residents in a U.S. Midwestern state, Rose et al. (2019) assessed the relative weights of risks and benefits of genetically modified foods and explored the implications of results on the future of gene-editing in food production. Risks have a greater weight than benefits on GM food rejection, specifically risks associated with manufacturers benefitting from GM foods as well as GM foods causing allergies or illness. In addition, they found media coverage, and subsequent perceived familiarity with GM foods, to be interconnected with GM food rejection. The researchers argued
103 Texas Tech University, Nellie Hill, August 2020 these results carry implications for acceptance of food products made possible by gene- editing (Rose et al., 2019).
Metaphors for Gene-Editing in the Media
Pervasive in daily conversation and formal communication alike, metaphors are useful in creating a relationship between the science community and the public (Taylor &
Dewsbury, 2018). Metaphors are a critical component of language, especially when communicating new, complex, and often intangible concepts of science (Thibodeau et al.,
2019). They direct the attention and cognitive processes of the metaphorical message receiver (Moser, 2001). A metaphor, such as “spilling the beans,” activates connections with tangible items (beans) as well as a relational system (of revealing secrets) (Gentner,
1982; Gibbs, 2005).
News media often utilize metaphors as they allow for economy of language, which is especially important when writing online (Witschge & Nygren, 2009). Steen et. al. (2010) found 16.4% of words used in news writing are metaphorical. Metaphors specific to gene-editing are pervasive in news media. As an example, O’Keefe et al.
(2015) found newspapers and magazines likened CRISPR technologies to the development of HTML webpage markup language in the news media and magazines.
This comparison indicates a widely applicable, technological advance can change the world (O’Keefe et. al., 2015).
Exploration of U.S. news media has identified common metaphors for describing gene-editing. O’Keefe et al. (2015) identified a range of metaphors for gene-editing used by large-circulation newspapers as well as popular science publications when reporting on human and agricultural applications of CRISPR systems. The most prevalent
104 Texas Tech University, Nellie Hill, August 2020 metaphors were describing the genome as “text” for editing with CRISPR and “targeting” as a means of describing how CRISPR works. Other metaphors described the technology as a blueprint/construction, code, gambling, map, medicine, origami, as well as war/battle/fight. The researchers called on future research to investigate how these metaphors would be received in actual use (O’Keefe et al., 2015).
Marcon et al. (2019) explored how CRISPR was portrayed in U.S. and Canadian popular press news articles. The metaphor of gene-editing as a tool was found in 98.7% of the articles. Half of those articles further detailed the functionality of the gene-editing tool. The articles repeatedly relied on metaphors such as “(molecular) scissors”, and a
“cut-and-paste” or a “search-and-replace” tool like a “word processor” to describe how
CRISPR works (Marcon et al., 2019).
Hill (2020) identified seven metaphorical concepts used by four U.S. newspapers in online content when reporting on gene-editing in agriculture. The most prevalent concepts were gene-editing as creation, as a text editor, and as a tool. In lesser frequency, additional metaphors used by the media were gene-editing as a coding program, a fighter, math, and targeting. Though these are the prevalent metaphors present in media coverage, it is not known which are the most effective in terms of causing an audience to give considerable thought to the topic (Hill, 2020).
Although previous research has identified common metaphors used to describe gene-editing, empirical studies testing the persuasive effects of these metaphorical concepts on receivers is lacking. Corner and Pidgeon (2014) empirically tested describing genome engineering as analogous to a natural process. It is important to note the participants in the study were from the United Kingdom, where regulation of gene-
105 Texas Tech University, Nellie Hill, August 2020 editing differs from the United States (Metje-Sprink et al., 2019). Nevertheless, this study found participants who read a description likening genome engineering to a natural process were more likely to support genome engineering that those who read the control description (Corner & Pidgeon, 2014).
Paris and Glynn (2004) used a three (animal cell, human eye, and electrical circuit) by three (no analogy, simply analogy, and elaborate analogy) experimental design to determine which best improved the science knowledge and attitudes of preservice teachers. Elaborate analogies, those containing text and pictorial components, were related to the most improvement (Paris & Gynn, 2004). Scholars of language often equate analogy to metaphor (Gentner, 1982).
There is more criticism of current metaphorical concepts for gene-editing than testing the actual use and outcomes of the concepts (Nelkin & Lindee, 2004; O’Keefe et al., 2015; Perrault & O’Keefe, 2019; Pigliucci & Boudry, 2011; Taylor & Dewsbury,
2018; Rothman, 2001). As detailed in previous literature review, science and the media has already proliferated entrenched metaphors. This study tested common metaphorical concepts for their persuasive effects in anticipation that future research will strengthen the most compelling concepts into metaphors that adequately convey the operational steps, uncertainty, and potential value of the technology (O’Keefe et al., 2015; Perrault &
O’Keefe, 2019; Taylor & Dewsbury, 2018).
Metaphors are not a silver bullet to create understanding. Receivers of a metaphorical message draw upon their own personal understandings to interpret and respond to the message (Condit 1999; Condit et al. 2002; Gronnvoll & Landau 2010).
Metaphors are particularly powerful, if we can harness them, in creating understanding of
106 Texas Tech University, Nellie Hill, August 2020 gene-editing because it cannot be directly observed (Gronnvoll & Landau 2010).
Researchers have called for continued investigation of the metaphors used to describe genome engineering as the technology evolves and cultural meanings of metaphors evolve as well (Blasimme et al., 2015; Nelson et al., 2016; O’Keefe et al., 2015; Taylor &
Dewsbury, 2018).
Conceptual Framework
The Elaboration Likelihood Model and structure-mapping theory formed the conceptual framework for this study. Metaphors stimulate thought because the message receiver uses a rich set of schemas to make connections between a familiar concept and an unfamiliar concept. The more connections are made, the greater the elaboration, and the greater the persuasive effects of the message (Gentner, 1982; Gentner, 1989; Sopory
& Dillard, 2002; O’Keefe, 2016; Petty & Cacioppo, 1986; Whaley, 1991; Wolff &
Gentner, 2011).
Elaboration Likelihood Model
The Elaboration Likelihood Model refers to the extent and probability that an individual will consider a persuasive message. Elaboration is “engaging in issue-relevant thinking” (O’Keefe, 2016, p. 149). Persuasion of an individual can be achieved through high elaboration using the central, systematic processing route or low elaboration using the peripheral, heuristic processing route (O’Keefe, 2016). High elaboration, characterized by deep cognitive consideration of a persuasive message, is more likely to achieve an attitude that predicts behavior, endures over time, and resists counter- persuasion (O’Keefe, 2016; Perloff, 2008). Creating and testing messages that promote cognitive processing is the key to attitude and behavior change (McGuire, 1989).
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Researchers have explored behavioral intentions and outcomes related to persuasive, metaphorical, communications concerning topics in academics, advertising, health, and politics (Van Stee, 2018).
The use of the central versus peripheral processing routes is dependent on the motivation and ability of the message receiver (Petty & Cacioppo, 1986). These two factor groups, motivation and ability, must be satisfied in order for a receiver to engage in high elaboration regarding a persuasive message. The motivation of the receiver is determined by their personal involvement with the topic and need for cognition. As personal involvement, or the relevance of the topic to the receiver, increases so too does the receiver’s issue-relevant thinking regarding the message (O’Keefe, 2016; Petty et al.,
1981). The other determinant of a receiver’s motivation is their need for cognition which is “the tendency for an individual to engage in and enjoy thinking” (Cacioppo & Petty,
1982, p. 116). Those with a higher need for cognition have a greater motivation to elaborate when presented with a persuasive message (O’Keefe, 2016).
The other factor group affecting degree of elaboration is the receiver’s ability to engage in deep cognitive consideration of a message. Ability is determined by amount of distraction and prior knowledge of the receiver. Distraction from a persuasive message inhibits issue-relevant thinking. Prior knowledge of the persuasive message topic enables the receiver to engage in elaboration. As prior knowledge increases, so too does issue- relevant thinking. Ability and motivation combine to determine the degree of elaboration a receiver may engage in to process a persuasive message (O’Keefe, 2016).
How and what is said in a message also affects the elaboration and resulting persuasive effect of a message. The crafting of a message is concerned with three factors:
108 Texas Tech University, Nellie Hill, August 2020 the structure, the content, and the language (Perloff, 2008). Metaphors are a message factor and a linguistic tool used to craft intense, powerful language that has been shown to be more persuasive than powerless language (Perloff, 2008; Sopory & Dillard, 2002).
Metaphors should lead to greater elaboration, which utilizes the central processing route by demanding cognitive, issue-relevant thinking (Ortony, 1979; Sopory & Dillard, 2002).
Researchers have explained the persuasive effects of metaphors in terms of garnering audience attention, communicator credibility, relief, reduced counterarguments, stimulated elaboration, superior organization, and resource matching (Van Stee, 2018).
Sopory and Dillard (2002) conducted a meta-analytic review of the effects of metaphors on persuasion by analyzing pertinent studies published between 1983 and
2000. They found metaphorical language provides superior structure and organization to a message as well as increases receiver interest and concept associations in thoughts. This results in greater attitudinal change than a literal message (Sopory & Dillard, 2002).
These characteristics of cognitive processing indicate metaphors can encourage the use of central processing to validate a message. In short, “metaphors enhance persuasion”
(Sopory & Dillard, 2002, p. 382).
As an update to Sopory and Dillard’s (2002) study, Van Stee (2018) conducted a meta-analysis of studies from 2001 to 2015 concerned with the persuasive effects of metaphorical messages versus literal messages. Of the 50 studies included in the analysis, only two (4%) focused on science related concepts or processes, indicating further investigation is needed regarding metaphor use in science communication (Van Stee,
2018). Thirty-four of the studies used a written message format, six used a visual message format, four studies combined written and visual message formats, one study
109 Texas Tech University, Nellie Hill, August 2020 used an audiovisual format, one study used an audio format, and four studies utilized a combination of multiple message formats. The analysis concluded that metaphorical messages are more persuasive than literal messages (Van Stee, 2018).
Structure-Mapping Theory
Structure-mapping theory presents metaphors as a system of connected knowledge which is used by a message receiver to map relations from a known topic to an unknown topic until a maximum structural match between the two topics has been reached (Gentner, 1982; 1983; 1989; Gentner & Bowdle, 2008). According to Gentner
(1982), a metaphor “asserts that identical operations and relationships hold among nonidentical things” (p. 4). It should be clear that structure-mapping is not a comparison of listed, independent attributes, but of the relational similarities held by the known topic to the unknown topic (Sopory & Dillard, 2002). As Sopory and Dillard (2002) explain the concept, “For example, the metaphor ‘Encyclopedias are goldmines’ is interpreted by noting the common relation ‘valuable nuggets found by digging’ rather than the independent similar attributes ‘valuable nuggets’ and ‘dig’” (p. 384).
Structure mapping occurs between a base system, the known domain, and a target system, the unknown domain (Gentner, 1982). Good scientific metaphors begin with
“base specificity” (Gentner, 1982, p. 12), meaning a known domain that is explicitly understood by the receiver of the metaphorical message. Therefore, a base system is most often a topic familiar to the receiver as this piece alone is the most critical to enabling structure-mapping. A good scientific metaphor has the structural characteristics of clarity, richness, abstractness, and systematicity. Finally, the scope and validity of a scientific metaphor must be considered. The scope of a metaphor determines its usefulness in
110 Texas Tech University, Nellie Hill, August 2020 application to scientific topics. The validity of a message is how true the operations and relationships of the base system are of the target system (Gentner, 1982). These features of a metaphor combine to enable a receiver to map meaning from the known to the unknown scientific topic.
Science communication often uses metaphor to explain the complexities in a way that allows the message receiver to make connections between their everyday experiences and scientific topics (Niebert & Gropengiesser, 2015). Gene-editing, the common name for DNA-free genome engineering, is itself a metaphor that conjures a system of connected knowledge related to the role and actions of an editor to improve upon something (O’Keefe et al., 2015; Hill, 2020). Though studies have identified additional metaphors to describe gene-editing, the literature does not reveal studies that specifically test the elaboration or persuasion elicited by different metaphorical messages (O’Keefe et al., 2015; Hill, 2020).
Using structure-mapping theory as a framework, Whaley (1991) proposed metaphors encourage the message receiver to utilize a greater number of memories to map meaning from the base system to the target system. This increase in associations yields a greater amount of issue-relevant thinking, leading to increased persuasiveness of the message given proper processing conditions are held by the receiver (Whaley, 1991).
Metaphors are a critical component of science communication (O’Keefe et al.,
2015; Taylor & Dewsbury, 2018). The news media, especially when publishing online, use metaphors to convey complex topics and connect the science community with the public (Marcon et al., 2019; Taylor & Dewsbury, 2018). The metaphors selected by news media affect public opinion (O’Keefe et al., 2015). Metaphorical messages cause more
111 Texas Tech University, Nellie Hill, August 2020 elaboration and persuasion than literal messages, but it is unknown which metaphors regarding gene-editing are most effective (O’Keefe et al., 2015; Sopory & Dillard, 2002;
Van Stee, 2018). The persuasive effect of the different metaphors used to explain how gene-editing in agriculture works should be tested in the online news environment
(Blasimme et al., 2015; Hill, 2020; Nelson et al., 2016; O’Keefe et al., 2015; Taylor &
Dewsbury, 2018).
Purpose and Research Questions
The purpose of this study was to determine the persuasive effects of metaphorical concepts regarding gene-editing in agriculture.
The following research questions guided the study along this purpose:
RQ1: How does the metaphorical concept used to explain gene-editing
applications in an agricultural context influence elaboration?
RQ2: How does the metaphorical concept used to explain gene-editing
applications in an agricultural context influence willingness to share the
information on social media?
Methodology
This study utilized a quantitative, randomized, between-subjects, experimental research design. This design is appropriate as it accounts for confounding variables to isolate the influence of the independent variable on the dependent variables. In addition, it supports investigating differences between groups and interpreting causal inferences
(Ary et al., 2010). The manipulation was four mock news articles differentiated by metaphorical concept for gene-editing in agriculture (creation versus text editor versus tool versus control) to explore the purpose and research questions of the study. The
112 Texas Tech University, Nellie Hill, August 2020 instrument used for data collection on May 15-18, 2020 can be found in Appendix A.
Institutional Review Board approval can be found in Appendix B.
Instrumentation
Qualtrics, an online survey building and delivery platform, was used to construct and disseminate the instrument for this study to ensure the sample reflected U.S. adults in terms of gender, race, education, and region. Randomization of four stimuli were built into the survey instrument as well as attention checks to ensure participants were providing thoughtful responses. The individual difference variables measured by the instrument were news consumption preferences, deference to scientific authority, attitude regarding gene-editing, factors affecting degree of elaboration, and coronavirus outbreak experience. The independent variable in the study was the metaphorical concept of gene- editing in agriculture embedded in a mock news article stimulus. The dependent variables in the study were elaboration, willingness to share on social media, willingness to consume, and perceived knowledge of gene-editing in agriculture. The demographics captured by the instrument were participant age, gender, level of education, political affiliation, and geographic location in terms of region as well as urban-rural classification. This research was conducted as part of a larger study; therefore, only a portion of the data is reported in this manuscript. The variables included in this aspect of the larger study are described below:
News consumption preferences. Eleven items were used to determine participant’s news, science news, and social media preferences. These items were adapted from a previous measure used by the Pew Research Center to determine where
Americans get science news (Funk et al., 2017). Participants were asked to answer items
113 Texas Tech University, Nellie Hill, August 2020 regarding how often they read news via online news sites, how often they read news online about science, and if they have a social media account. Participants who reported having a social media account were asked about how often they see science-related news on social media. Next, they were asked a series of seven items regarding how often, if ever, they take action such as liking, commenting or sharing posts.
Deference to scientific authority. The seven Likert-type items on the General
Social Survey’s scale were used to determine participant’s deference to scientific authority (Smith et al., 2015). This scale was included because deference to scientific authority can influence how people form opinions of agricultural biotechnology by way of “intervening orientation or behavior” (Brossard & Nisbet, 2007, p. 27). The scale asked participants to indicate their level of agreement with seven statements regarding science and technology on a seven-point Likert-type scale (1 = Strongly Disagree to 7 =
Strongly Agree). A sample statement from the measure was, Because of science and technology, there will be more opportunities for the next generation.
Bigham (2017) previously reported a reliability of the entire scale by a
Cronbach’s α = 0.561 before removing two items and raising the reliability coefficient to
Cronbach’s α = 0.754. Nunnally (1978) argued a reliability of 0.5 will suffice in the early stages of research. Kline (1999) stated that reliability values below 0.7 on psychological constructs are to be expected due to the diversity in measures.
Factors affecting degree of elaboration. Two factor groups, motivation and ability, were measured as they influence receiver engagement in elaboration regarding a persuasive message (O’Keefe, 2016). Motivation was measured by way of the need for cognition scale developed by Cacioppo et al. (1984). People with a higher need for
114 Texas Tech University, Nellie Hill, August 2020 cognition, or personal tendency to engage with and enjoy thinking, have a greater motivation to elaborate when presented with a persuasive message (Cacioppo & Petty,
1982; O’Keefe, 2016). Participants indicated their agreement with 18 items on a 5-point
Likert-type scale (1 = Strongly Disagree to 5 = Strongly Agree). A sample statement from the measure was, I like to have the responsibility of handling a situation that requires a lot of thinking. Cacioppo et al. (1984) reported the reliability coefficient of the scale is
Cronbach’s α =.90.
Ability was measured by assessing the participant’s perceptual and factual knowledge of gene-editing in agriculture. Five Likert-type items were adapted from
Critchley et al. (2019) and Gatica-Arias et al. (2019) to assess perceptual knowledge.
Three of the items were presented before stimulus exposure. After stimulus exposure, the participant was asked one of the questions from pre-exposure in addition to two new questions. The items asked participants to indicate their level of agreement with statements regarding their perceived knowledge of gene-editing in agriculture on a five- point Likert-type scale (1 = Strongly Disagree to 5 = Strongly Agree). A sample statement was, I feel I could explain gene-editing in agriculture to a friend.
Six true or false questions regarding genome engineering topics, including gene- editing in agriculture, were adapted from Scheufele et al. (2017) to assess the factual knowledge held by participants to determine their ability. A sample statement was,
Scientists have changed more than 30 genetic characteristics of commercially available plants with gene-editing (True).
Coronavirus outbreak experience. Four items were used to explore the influence of the COVID-19 pandemic on participant’s lives, well-being, and news
115 Texas Tech University, Nellie Hill, August 2020 consumption. These items were adapted from previous measures used by the Pew
Research Center (2020; Gottfriend et al., 2020) to determine how Americans are responding to the coronavirus outbreak. COVID-19 coverage consumed much of the news media during the time of this study (Gottfriend et al., 2020). In addition, the outbreak was found to have “profound impacts on the personal lives of Americans in a variety of ways” (Pew Research Center, 2020, para. 1). Therefore, this measure was included in this study to control for its influence on the dependent variables.
Metaphorical concept. The independent variable manipulated in this study was the metaphorical concept. The concept was operationalized as researcher-developed statements to reflect the top three metaphorical concepts found in online U.S. news by
Hill (2020). Three metaphorical statements were written as well as a literal, control statement. The control statement was verified for accuracy by a plant scientist and professor with expertise in gene-editing. All four statements were similar in terms of visual length as well as word count. The statements were created to be as alike as possible concerning persuasive message factors (Perloff, 2008).
Elaboration. Metaphors demand cognitive elaboration (Ortony, 1979). To assess elaboration in each participant, thought-listing was used immediately after stimuli exposure. This technique is perceived as a private, non-threatening, and non-reactive means of gathering of self-generated arguments without affecting reported behavior
(Cacioppo & Petty, 1981). Thought-listing was carried out as recommended by Cacioppo and Petty (1981) in terms of topic instruction, time limits, and delivery post-stimuli.
Immediately after the stimuli exposure, participants were asked to list all the thoughts they had while reading the mock news article. This sequence is an effort to replicate as
116 Texas Tech University, Nellie Hill, August 2020 closely as possible the affective and cognitive responses present in normal conditions and is the preferred method (Cacioppo & Petty, 1981; Burnett et al., 2019). Participants were presented 10 blanks to fill with the thoughts they had while viewing the article within the instructed five minute allotment, though there were instructed they did not have to fill every blank.
Behavioral intent measure. Attitudes developed through the central processing route are predictive of behavior (O’Keefe, 2016; Perloff, 2008). Four items adapted from
Stevens and McIntyre (2019) were used to assess the willingness and reasoning of each participant to share the mock news article they read on social media. Three items asked participants to indicate their level of likelihood to share the article on their Facebook,
Twitter, or another social media channel of choice on a six-point Likert-type scale (1 =
Definitely not to 6 = Definitely). Participants were then asked to describe their reasoning for sharing or not sharing the story on those channels.
Manipulation Check
The independent variable of the study, metaphorical concept, was embedded in researcher-developed stimuli. I developed four mock news articles in Adobe InDesign based on the journalistic reporting and metaphorical concepts identified in a previous systematic metaphor analysis of U.S. online news (Hill, 2020). The articles were standardized, presenting all of the same information with the exception of a metaphorical concept statement or control statement contained in the second paragraph. The control news article stimulus is presented in Figure 4.1, and the metaphorical concept mock news article stimuli are presented in Figures 4.2, 4.3, and 4.4.
117 Texas Tech University, Nellie Hill, August 2020
Figure 4.1
Mock News Article for Control
118 Texas Tech University, Nellie Hill, August 2020
Figure 4.2
Mock News Article for Metaphorical Concept of Creation
119 Texas Tech University, Nellie Hill, August 2020
Figure 4.3
Mock News Article for Metaphorical Concept of Text Editor
120 Texas Tech University, Nellie Hill, August 2020
Figure 4.4
Mock News Article for Metaphorical Concept of Tool
In order to establish the validity of the stimuli, a manipulation check was conducted prior to deploying data collection for the primary experiment of the study. The purpose of the manipulation check was to ensure each stimulus represented the metaphorical concept it was designed to present.
To carry out the manipulation check, a Qualtrics questionnaire was sent to graduate students in the Texas Tech University Department of Agricultural Education and Communications, a group not included in the sample population. The questionnaire randomly presented all four mock news articles to each participant. The participants were instructed to read each news article and answer the same question after each news article: 121 Texas Tech University, Nellie Hill, August 2020
Which metaphor was present in the article you read? Participants then responded by selecting one of the following choices: Gene-editing is creation, Gene-editing is editing text, Gene-editing is a tool, or None of these. Each response was designed to represent one of the mock news articles.
The manipulation check questionnaire was completed by 33 participants. Gene- editing is creation was correctly identified by 50.0% of participants. Gene-editing is editing text was correctly identified by 86.7% of participants. Gene-editing is a tool was correctly identified by 93.3% of participants. The control, None of these, was correctly identified by 36.7% of participants. The mock news article stimuli that presented the metaphors of gene-editing as tool and as a text editor resulted in satisfying levels of agreement. The mock news article stimulus designed to present the metaphor of gene- editing as creation was revised to include additional language found by Hill (2020) to represent the metaphorical concept of creation. The revised gene-editing as creation stimuli was sent alongside the control stimuli to a subset of the original participants to confirm the revision strengthened the difference between the two mock news articles.
Respondents confirmed the improvement of the stimuli and so all stimuli were confirmed for entry into the pilot phase of the study.
Participants
With regards to the sample for full analysis, a nationally representative population was selected for this study because the intent was to explore which metaphorical concept(s) have the most persuasive effects on the general public in the United States.
Qualtrics was used for crowdsourcing participants for the study who were nationally representative in terms of gender, education, race, and geographic region.
122 Texas Tech University, Nellie Hill, August 2020
Participants for this study were recruited and compensated through Qualtrics. The online research company recruited participants that met the study’s population parameters and compensated them per Qualtrics policies. We paid Qualtrics $5.00 per complete questionnaire, then Qualtrics monetarily compensated the participants according to their internal policies. A total of 315 responses were recorded. However, 15 responses were removed from the sample for including gibberish or nonsensical responses to open-ended text questions resulting in N = 300 usable responses for subsequent analysis.
Procedure
The instrument was reviewed for face and content validity by a panel of experts in agricultural communications as well as plant and soil science prior to launch. A pilot test was conducted through Qualtrics to establish reliability of the instrument used.
Participants were a random sample of adults in the U.S (N = 25), recruited and compensated by Qualtrics. All data were reviewed, and wording adjustments were made to some of the items in the instrument. Cronbach’s alpha was used to determine reliability of the researcher-developed measures, perceived knowledge and willingness to share the article information on social media post-stimuli. The reliability coefficient for perceived knowledge was α = 0.667. The reliability coefficient for willingness to share on social media post-stimuli was α = 1.00. Although acceptable alpha values are typically .8 or greater (Kline, 1999), Nunnally (1978) argued in the early stages of research values as low as .5 will suffice. As the measure only included three items, and the responses for one item were reverse listed, all three were included in the future analysis.
Using the Qualtrics platform, each participant was first presented with a brief description of the research, then opted to participate in the study by clicking on a link to
123 Texas Tech University, Nellie Hill, August 2020 begin the questionnaire. Participants responded to five demographic items, followed by eleven items regarding their news consumption preferences, then they responded to seven
Likert-type items regarding their deference to scientific authority. Then, the participants’ perceptual and factual knowledge of gene-editing in agriculture was assessed by way of three Likert-scale items and six true/false items. Next, participants were presented with a randomly assigned stimulus, the experimental treatment of the study operationalized as a mock online news article.
After reading the randomly assigned stimulus, participants were asked to list all of their thoughts while reading the mock news article. Next, participants responded to four items regarding their willingness to share the article on social media. Participants were asked three perceptual knowledge questions, then two more demographic questions.
Then, participants were asked four items regarding their coronavirus experience, then completed the need for cognition scale containing 18 items. Participants were thanked for their responses and informed that the news article they read was researcher created.
Data Analysis
Of the participants (N = 300), 76 viewed the control mock news article stimulus,
76 viewed the text editor stimulus, 75 viewed the creation stimulus, and 73 viewed the tool stimulus. Descriptive statistics were used to describe the sample population. In order to address the research questions, individual ANCOVAs were conducted to assess the effects of the independent variable, metaphorical concept, on elaboration and willingness to share on social media. Demographic data as well as individual difference variables data were used as covariates when appropriate to address the research objectives of this study.
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Demographics. The demographic information collected from each participant was their age, gender, level of education, political affiliation, and geographic region as well as urban-rural classification. Slightly more of the participants identified as a female, accounting for 151 (50.3%) of the participants while 149 (49.7%) identified as a male.
The majority of the participants, 201 (67%), were white. Thirty-five (12%) participants were Black or African American, 33 (11%) were Hispanic, 24 (8%) were Asian, 4 (1%) were Native Hawaiian or Pacific Islander, 2 (1%) chose to select “Other”, and 1 (.3%) was American Indian or Alaska Native. The average age of the participants was 48.1 (SD
= 17.1) with a minimum age of 18 and a maximum age of 88.
Two geographic questions were asked of the participants. The largest percentage of participants indicated their permanent residence is in a state in the southern region of the United States, accounting for 122 (41%) of the participants. Sixty-two (21%) participants were from the Midwest, 59 (20%) were from the Northeast, and 57 (19%) were from the West. The majority of participants, 154 (51%), reported living in a urbanized area of 50,000 of more people. Eighty-two (27%) participants indicated their permanent residence is in an urban cluster of at least 2,500 and less than 50,000 people.
Sixty-four (21%) participants permanently reside in a rural area.
Participants were asked about the level of education they had attained. Sixty- seven (22%) participants have a four-year degree. Professional degrees are held by 61
(20%) participants. Another 61 (20%) participants are high school graduates. Fifty-five
(18%) participants reported attending some college. Forty-one (14%) participants have a two-year degree, 12 (4%) have a doctorate, and 3 (1%) have less than high school educational attainment.
125 Texas Tech University, Nellie Hill, August 2020
Political ideology information was gathered from the participants in the study.
The largest percentage of participants reported being middle of the road in their ideology, accounting for 78 (26%) of the participants. Fifty (17%) participants indicated they were liberal, 46 (15%) were conservative, 39 (13%) were strongly conservative, 34 (11%) were strongly liberal, 27 (9%) were slightly conservative, 21 (7%) were slightly liberal, and five (2%) participants reported having some other political ideology.
Individual difference variables. News consumption preferences, deference to scientific authority, need for cognition and knowledge were measured as individual difference variables in this study.
News consumption was explored through a series of questions about participant’s news, science news, and social media preferences. The greatest number of participants (n
= 124, 41.3%) reported reading the news on an online news website several times a day.
When asked specifically about how often they read news online about science, 67
(22.3%) participants indicated a few times per week.
The majority of participants (n = 272, 90.7%) reported having a social media account. When asked about how often they see science-related posts on their social media platforms, 117 (39.0%) participants indicated sometimes. Participants were asked a series of questions regarding how often they take an action when they see science news on social media. Of the participants, 110 (36.7%) indicated they would sometimes click on a link to access an online article. Eighty-nine (29.7%) responded they would sometimes comment on the post. Ninety-five (31.7%) indicated they would sometimes like the post, while 90 (30.0%) said they would often like the post. Ninety-one (30.3%) of participants indicated they would never ignore or hide the science news post. When asked how often
126 Texas Tech University, Nellie Hill, August 2020 they would unfollow or block the person or entity that provided the post, 144 (48%) indicated never. When asked how often they would share the post to show it is wrong,
116 (38.7%) of participants indicated never. When asked how often they would share the post to show it is right, 88 (29.3%) of participants indicated sometimes. For each participant, the responses to the two items questioning how often they share a post were averaged for a measure of individual willingness to share on social media pre-stimuli (M
= 2.46, SD = 1.03). Higher mean values indicate a greater willingness to share on social media.
Deference to scientific authority was measured using the scale from the General
Social Survey (Smith et al., 2015). A post hoc reliability analysis established a
Cronbach’s α = 0.69. Two items were removed (Science makes our way of life change too fast and Scientists are apt to be odd and peculiar people), which raised the reliability coefficient to a Cronbach’s α = 0.92. Bigham et al. (2017) also removed these two items to raise the reliability coefficient of the scale. For each participant, the responses to each of the seven items were averaged for a measure of individual deference to scientific authority (M = 5.49, SD = 1.48). Deference to scientific authority ranged from 1.00 to
7.00. Higher mean values indicate higher deference to scientific authority.
The need for cognition scale was used as a measure of motivation (Cacioppo et al., 1984). A post hoc reliability analysis established a Cronbach’s α = 0.86. Removal of items from this scale did not increase reliability so all 18 items remained intact. Nine of the 18 items were reverse coded, then each participant’s responses were averaged for a measure of individual need for cognition (M = 3.14, SD = .60). Need for cognition ranged
127 Texas Tech University, Nellie Hill, August 2020 from 1.22 to 4.94. Higher values indicate greater need for cognition held by the individual.
The elaboration factor of ability was measured by assessing the perceptual and factual knowledge of gene-editing in agriculture held by each participant. Perceived knowledge was assessed with three items before the stimuli. A post hoc reliability analysis of the items established a Cronbach’s α = 0.620. This was a researcher- developed measure deployed for the first time, so items were not removed to increase reliability (Nunnally, 1978). Each participant’s responses were averaged for a measure of their perceptual knowledge of gene-editing in agriculture pre-stimuli exposure (M = 2.98,
SD = .96). Perceptual knowledge ranged from 1.00 to 5.00. Higher values indicate greater perception of personal knowledge regarding gene-editing in agriculture.
The average factual knowledge score of the participants was determined by scoring the responses to six true or false statements delivered prior to the stimulus, then determining a percentage of questions correct. The average factual knowledge score was
64.7% (SD = 21%). Factual knowledge scores ranged from 17% to 100%.
The influence of the COVID-19 pandemic on participants’ lives, well-being, and news consumption was assessed as data collection was conducted during the coronavirus outbreak, May 15-18, 2020. When asked about how the outbreak has affected their own personal life, 146 (48.7%) participants indicated their lives changed in a major way.
When asked about how often in the last seven days they have experienced a physical reaction to thinking about the outbreak, the majority of participants indicated they rarely or none of the time (less than 1 day) (n = 195, 65.0%). More than half of participants (n =
153, 51.0%) indicated they have been following the news about the outbreak known as
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COVID-19 very closely. When asked how the amount of news they read via an online news sites changed during the outbreak, 128 (42.7%) participants indicated they read such news more often.
Dependent variables. Elaboration was measured through a thought-listing exercise immediately after participants read the stimulus mock news article. Cacioppo and Petty (1981) stated the most consistent measure of coding thought-listing is using the polarity dimension of positive, neutral, or negative thoughts. It is important to note, though the researchers use the words positive, neutral, and negative, the code indicates the attitude of the thought as well as its relevance to the message (Cacioppo & Petty,
1981). Positive thoughts as those “in favor of the referent that mention specific desirable attributes or positive associations, statements that support validity or value of situation/stimulus and statements of positive effect” (Cacioppo & Petty, 1981, p. 319).
Neutral thoughts are those that “express no affect with regards to the referent,” (Cacioppo
& Petty, 1981, p. 319). Negative thoughts are those that, “mention specific undesirable attributes or negative associations, challenges to the validity of the stimulus or situation, and statements of negative affect” (Cacioppo & Petty, 1981, p. 319).
Coding was conducted by two independent judges, graduate students, because
“independent judges have demonstrated a high degree of agreement in their classification of responses along the polarity dimension” (Cacioppo & Petty, 1981, p. 325). The judges were trained how to code polarity by first reading the definitions of the codes set forth by
Cacioppo and Petty (1981) and asking any clarifying questions. Then the judges coded a random sample of 10% (n = 30) of the participants’ thought-listings. Judges coded each
129 Texas Tech University, Nellie Hill, August 2020 thought provided by 30 participants as positive, negative, or neutral with regards to the relevance and attitude of the thought.
Krippendorff’s alpha test was used to estimate the inter-judge reliability
(Krippendorff, 2011). When measuring reliability using Krippendorff’s alpha, a reliability coefficient greater than .80 is ideal, but alphas as low as .667 are considered acceptable for gathering tentative conclusions (Krippendorff, 2004). After initial coding, judges showed low reliability, with α = .68. In order to reach an acceptable level of intercoder reliability, additional coder training was conducted which was a discussion of discrepancies as well as any uncertainties the judges experienced while coding. Judges were reassigned a new random sample of 10% (n = 30) of the participants’ thought- listings. After the second round, an acceptable alpha level was reached with judges showing high reliability, with α =.86. Each judge was then assigned an equal share of the remaining thought-listings to code. Elaboration was operationalized as the number of total relevant thoughts, positive or negative, expressed by each participant (O’Keefe,
2016). Neutral thoughts are not issue-relevant (Cacioppo & Petty, 1981). Participants reported an average of 3.26 thoughts (SD = 2.46), with the number of relevant thoughts reported ranging from 0 to 10 for all participants.
The behavioral intent measure in this study was the participant’s willingness to share the mock news article they read on their social media accounts. Participants were asked to indicate their level of likelihood to share the article on their Facebook, Twitter, or another social media channel of choice. Only responses provided by participants who indicated they had a social media account were included in analysis. A post hoc reliability analysis of the items established a Cronbach’s α = 0.873. Each participant’s
130 Texas Tech University, Nellie Hill, August 2020 responses were averaged for a measure of their willingness to share the mock news article
(M = 3.27, SD = 1.95), indicating participants were overall somewhat unlikely to share the article on social media platforms. If the participant noted a social media channel of choice in addition to Twitter and Facebook, their likelihood to share on that channel was included in their average score.
Results
Guided by structure-mapping theory and the Elaboration Likelihood Model, the purpose of this study was to investigate the persuasive effects of metaphorical concepts for gene-editing in agriculture by measuring elaboration and willingness to share on social media.
RQ1: How does the metaphorical concept used to explain gene-editing applications in an agricultural context influence elaboration?
A one-way ANCOVA was conducted to determine if there was a statistically significant difference between exposure to the control, creation, text editor or tool metaphorical concepts on elaboration. The covariates in this analysis were age, gender, education, race, political ideology, geographic region, deference to scientific authority, factual knowledge, pre-stimuli perceptual knowledge, need for cognition, how often the participant reads online new sites, and how news consumption has changed during the
COVID-19 outbreak. These covariates were included because of their known effects on elaboration, especially in the context of science. Analysis was guided by Field (2017) and
Laerd Statistics (2017). Independent ANOVA analyses were conducted to ensure independence of all covariates and the treatment effect. Revealed by the ANCOVA model, the assumption of homogeneity of variances was violated, as assessed by Levene's
131 Texas Tech University, Nellie Hill, August 2020 test for equality of variances (p = .013). Inspection of histograms revealed elaboration was positively skewed, so the data were converted using a square root transformation
(Laerd, 2017).
Using the transformed elaboration variable in the ANCOVA model, assumptions of homoscedasticity and homogeneity of regression slopes were evaluated and met. There was homogeneity of variances, as assessed by Levene's test for homogeneity of variances
(p = .454).
Data presented are adjusted means and standard error. Elaboration was highest in the text editor metaphorical concept group (M = 1.72, SE = .09) compared to the creation metaphorical concept (M = 1.68, SE = .09), control stimuli (M = 1.57, SE = .09), and tool metaphorical concept (M = 1.52, SE = .09). There were no significant differences in elaboration between the treatment groups (p = .28, η2 = .014). The inferential statistics reported for this ANCOVA are shown in Table 4.1.
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Table 4.1
Analysis of Covariance of Elaboration Regarding Gene-Editing in Agriculture, with Individual Difference Variables as Covariates
Source df SS MS F p η2 Metaphorical Concept 3 2.13 .71 1.30 .28 .014
Covariates Need for cognition 1 4.85 4.85 8.89 .003 .030
Factual knowledge 1 1.91 1.91 3.49 .063 .012
Perceptual knowledge 1 2.36 2.36 4.33 .038 .015
Deference to scientific 1 2.23 2.23 4.08 .044 .014 authority
Online news reading 1 2.20 2.20 4.03 .046 .014
COVID-19 news change 1 2.68 2.68 4.91 .028 .017
Age 1 4.03 4.03 7.39 .007 .025
Gender 1 1.42 1.42 2.61 .108 .009
Education 1 .03 .03 .04 .822 .000
Race 1 .11 .11 .21 .651 .001
Political ideology 1 .12 .12 .21 .645 .001
Region 1 .03 .03 .06 .806 .000
Error 284 155.03 .546
Total 300 979.00
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RQ2: How does the metaphorical concept used to explain gene-editing applications in an agricultural context influence willingness to share the information on social media?
A one-way ANCOVA was conducted to determine if there was a statistically significant difference between exposure to the control, creation, text editor or tool metaphorical concept on willingness to share the information on social media. Only participants who reported having a social media account were included in analysis (n =
272). The covariates in this analysis were age, gender, education, race, political ideology, geographic region, urban/rural classification, willingness to share on social media measured pre-stimuli exposure, elaboration, and how closely the participants reported following the news about the COVID-19 outbreak. These covariates were included because of their known effects on willingness to share on social media. Analysis was guided by Field (2017) and Laerd Statistics (2017). Independent ANOVA analyses were conducted to ensure independence of all covariates and the treatment effect. Assumptions of homoscedasticity and homogeneity of regression slopes were evaluated and met. There was homogeneity of variances, as assessed by Levene's test for homogeneity of variances
(p = .394).
Data presented are adjusted means and standard error. Willingness to share the information regarding gene-editing in agriculture was highest in the tool metaphorical concept group (M = 3.54, SE = .19) compared to the control stimuli (M = 3.41, SE = .17), creation metaphorical concept (M = 3.07, SE = .18), and text editor metaphorical concept
(M = 3.07, SE = .18). There were no significant differences between the treatment groups
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(p = .15, η2 = .020). The inferential statistics reported for this ANCOVA are shown in
Table 4.2.
Table 4.2
Analysis of Covariance of Willingness to Share on Social Media Information Regarding Gene-Editing in Agriculture, with Individual Difference Variables and Elaboration as Covariates
Source df SS MS F p η2 Metaphorical Concept 3 11.51 3.84 1.77 .153 .020
Covariates Elaboration 1 1.60 1.60 .74 .394 .003
Willingness to share pre 1 242.85 242.85 112.32 <.001 .302
COVID-19 news 1 19.03 19.03 8.80 .035 .033 following
Age 1 37.60 37.60 17.39 <.001 .063
Gender 1 .13 .13 .06 .809 .000
Education 1 5.35 5.35 2.46 .117 .009
Race 1 .56 .56 .26 .612 .001
Political ideology 1 1.66 1.66 .77 .382 .003
Region 1 .11 .11 .05 .824 .000
Urban/Rural 1 1.14 1.14 .76 .385 .003
Error 259 560.01
Total 272 3971.08
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Discussion, Conclusions & Recommendations
The purpose of this study was to determine the persuasive effects of metaphorical concepts regarding gene-editing in agriculture. Metaphors have been found to enhance elaboration and influence behavioral intentions (Corner & Pidgeon, 2015; Hongyan &
Sixian, 2019; Johnson & Taylor, 1981). However, this study found changing the metaphorical concepts for gene-editing in agriculture did not elicit these outcomes.
RQ1 found no significant differences between the metaphorical concepts on participants’ elaboration, or issue-relevant thoughts, regarding gene-editing in agriculture. Perhaps with additional controls for factors affecting elaboration, such as attitude and involvement with the issue, a significant difference between conditions may have been viewed. Without the ability to control for noise, another factor affecting elaboration, the topic may not have been relevant enough to the participants to overcome distractions while completing the questionnaire. The ability to elaborate is also enhanced by repetition (Perloff, 2008). Future research using a similar design may want to further integrate the metaphorical concept throughout the stimuli. The direction of participants’ elaboration (i.e. positive or negative) was not analyzed in this study, so future research may warrant identifying if a significant difference in attitude lies between treatment groups. Furthermore, the elaborative thoughts of the participants could be investigated in terms of their origin dimension and target dimension as recommended by Cacioppo and
Petty (1981).
RQ2 found no significant differences between the metaphorical concepts on participant’s willingness to share information regarding gene-editing in agriculture. As the topic of gene-editing grows in the popular press, so too may willingness to share
136 Texas Tech University, Nellie Hill, August 2020 information about it on social media. As people are still forming opinions about gene- editing in agriculture, they may not yet be ready to share information about it on their social media pages (Brossard, 2018; Miller, 2004; Rainie, 2017). Previous research suggests people share information on social media in an effort to build their identity
(Berger, 2014; Boyd & Ellison, 2007). Sharing on social media has been found to indicate agreement with the message (Grabbert et al., 2019; Kee et al., 2016). Participants in this study may still be forming an opinion about gene-editing in agriculture, even after viewing the informative article. Additional research should investigate persuasive effects of metaphorical concepts on other behavioral outcomes associated with elaboration, such as willingness to tell friends and family or willingness to consume gene-edited foods.
Ability is also an indicator of elaboration, so the perceptual outcomes of enhanced ability through perceived knowledge assessment should also be assessed in future studies.
This study only explored elaboration on three metaphorical concepts commonly found in U.S. online news. Additional metaphors should be explored for their ability to persuade as well as their correctness in explaining the complex science of gene-editing in agriculture (Perrault & O’Keefe, 2019). Future research should also test the metaphorical concepts in other forms of media, such as social media posts, blogs, television broadcasts, podcasts and public service announcement contexts. The use of visual metaphors for gene-editing in agriculture should also be explored for their effect on elaboration and behavioral outcomes as they may differ from written metaphors.
While this study did not reveal any significant differences between the metaphorical concepts, researchers and practitioners can still draw important lessons and guidelines for communicating science to the public from the theoretical and literary
137 Texas Tech University, Nellie Hill, August 2020 foundations of this study. It is important to test the structure-mapping theory of metaphor and the elaboration likelihood model to find out how the theories operate in contexts with as much environmental reliability as possible. Understanding how the public responds to messages enhances communicators’ ability to develop effective messaging that does influence elaboration and desirable behavioral outcomes. Gene-editing in agriculture is still a relatively novel topic and should be studied over time to understand how public opinion of the topic evolves and can be influenced by communications efforts.
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CHAPTER V
IMPLICATIONS & RECOMMENDATIONS
The overarching purpose of this study was to understand how the United States’ public and news media discuss gene-editing applications in agriculture and what impact the context in which the topic is discussed has on public opinion. To accomplish this purpose, three independent, yet interconnected, research phases were conducted to describe the public discussion, examine the news media discussion, and test means of persuading public thinking and discussion about gene-editing in agriculture. The three phases each addressed a separate purpose with unique research questions:
A Descriptive Analysis of Twitter Content Regarding the Use of Gene-Editing in
Agriculture (Phase One)
RQ1: How many mentions of gene-editing applications in agriculture were
publicly posted on Twitter between September 1, 2018 and December 31, 2019?
RQ2: What was the social reach and engagement of those tweets?
RQ3: What was the sentiment of those tweets?
RQ4: How does tweet reach and engagement vary based on tweet sentiment?
A Systematic Metaphor Analysis of Gene-Editing in Agriculture in Online United
States News (Phase Two)
RQ1: What metaphorical concepts do online U.S. news articles use to frame gene-
editing applications in agriculture?
RQ2: What are the most prominent metaphorical concepts used in those articles?
148 Texas Tech University, Nellie Hill, August 2020
Persuasive Effects of Metaphors Regarding Gene-Editing in Agriculture (Phase
Three)
RQ1: How does the metaphorical concept used to explain gene-editing
applications in an agricultural context influence elaboration?
RQ2: How does the metaphorical concept used to explain gene-editing
applications in an agricultural context influence willingness to share the
information on social media?
Overview of Study Phases
Phase One
Meltwater, a social media monitoring platform, was utilized to collect relevant, publicly posted tweets so as to facilitate a quantitative, descriptive analysis of the content related to gene-editing applications in agriculture. The Meltwater monitor was established to gather tweets that contained the following root keywords: “CRISPR”,
“gene-editing”, “gene editing”, and “genome editing”, or “TALENs”. Each root keyword also required at least one of the following keywords: “agriculture”, “animal”, “livestock”,
“crop”, “food”, and “plants”. The monitor excluded tweets with the keywords: “baby”,
“babies”, and “human”. Meltwater provided a report containing all of the collected tweets, which were analyzed using descriptive and non-parametric statistics. The tweets were evaluated for their number, reach, engagement, and sentiment.
Phase Two
The results of Phase One indicated news media outlets hold the greatest potential to reach the public with information regarding gene-editing in agriculture. Therefore,
Phase Two sought to investigate how the news media is explaining the topic. The Nexis
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Uni database was utilized to collect U.S. news media articles published online concerning gene-editing in agriculture so as to facilitate a qualitative systematic metaphor analysis.
An article search in the database was conducted for the root keywords of “gene-editing”,
“gene editing”, and “genome editing”. Each root keyword also required at least one of the following keywords: “agriculture”, “animal”, “livestock”, “crop”, “food”, and “plants”.
The search excluded articles with the keywords “baby” and “babies”. Based on the search results, The New York Times, Washington Post, The Atlantic, and The Associated Press were chosen to represent national news media as they published the most content pertaining to the topic within the timeframe of the study. The 26 collected news articles underwent a data-driven coding process to identify the metaphors used to describe gene- editing in agriculture in terms of process or product. The metaphors were then analyzed to develop a list of underlying metaphorical concepts and their frequency of occurrence.
Phase Three
The results of Phase One indicated the public is still in the innovation decision making process when it comes to gene-editing in agriculture. The results of Phase Two indicated the most commonly used metaphors to describe the topic to the public.
Therefore, Phase Three utilized a quantitative, randomized, between-subjects, experimental survey research design to facilitate exploration of which metaphorical concept causes the most issue-relevant thinking about gene-editing in agriculture as well as the most willingness to share the information on social media. The most common metaphorical concepts found in phase two (gene-editing as creation, text editor or tool) were embedded into a researcher-developed mock news article with links to share on social media. An additional control mock news article was created. Prior to being
150 Texas Tech University, Nellie Hill, August 2020 instructed to read one of the randomly assigned news articles, participants provided demographic information as well as responded to items regarding their deference to scientific authority, perceived knowledge of the topic, and factual knowledge of the topic.
After viewing the stimuli, the participants were asked to share all the thoughts they had while reading the article, then indicate their willingness to share the article on social media. Finally, participants responded to items regarding their experience during the coronavirus outbreak and their need for cognition.
Implications
Phase One
Between September 1, 2018 and December 31, 2019, the amount of conversation regarding gene-editing in agriculture, the number of contributing Twitter users, and the reach of the conversation was relatively stable. In contrast, engagement with the conversation was on the rise and the sentiment of tweets was becoming increasingly positive. The findings suggest users are indicating they are exchanging information and making decisions about gene-editing in agriculture by increasing their conversation participation in the form of replies, retweets, and likes and greater positivity. These actions are indications of movement through the innovation-decision process (Kee et al.,
2016; Rogers, 2003). Results indicated the conversation around gene-editing in agriculture is stable yet growing in interaction and positivity.
The accounts creating the content with the most reach into the conversation were almost exclusively news media accounts. Of the top ten accounts with the greatest reach, nine represented a news platform, six of which have a print magazine or newspaper component. Mass media channels, such as these, hold the greatest opportunity to
151 Texas Tech University, Nellie Hill, August 2020 influence knowledge acquisition about gene-editing in agriculture by linking Twitter users to information outside of the social system (Rogers, 2003). Results indicated positively toned messages tended to reach a wider audience than neutrally toned tweets, but otherwise there were no significant differences between sentiment and tweet reach.
Additional investigation of how tonality of tweets affects information diffusion is needed
(Zhu et al., 2020).
Engagement with the conversation on Twitter is a combination of retweets, replies and likes. Conversation engagement can be an indicator of interaction among the social system as well as agreement with the message (Grabbert et al., 2019; Kee et al., 2016).
More than half of the tweets in this study were retweets, yet engagement was relatively low compared to the number and reach of tweets in the conversation. Higher engagement was associated with positively-toned tweets. A variety of account types, including individuals, posted the tweets with the highest engagement. Twitter users involved in the conversation about gene-editing in agriculture are likely to be mostly still in the knowledge stage of the innovation-decision process because engagement and sentiment remains primarily stable and neutral over time though results indicate decision-making is progressing. Greater engagement as well as more instances of tweets with positive or negative sentiment could be an indicator of decision making about gene-editing in agriculture (Rogers, 2003).
Phase Two
The articles collected between January 2, 2015 and June 11, 2019 from the four
U.S. news sources revealed seven underlying metaphorical concepts used to describe gene-editing in agriculture in terms of process or product. The articles conceptualized the
152 Texas Tech University, Nellie Hill, August 2020 topic as creation, a coding program, a fighter, math, targeting, a text editor, and a tool.
Some of the concepts are new to the literature, while some of the same metaphorical concepts have been found by previous research of metaphors used to describe gene- editing in general (Condit, 1999; O’Keefe et al., 2015). Overall, the concepts seem to convey a sense of complexity, detail, and skill required by gene-editing technologies in agriculture.
Each metaphorical concept provides a lens through which an audience can view the unknown topic of gene-editing in agriculture using their own everyday experiences to understand it (Entman, 1993; Goffman, 1974; Price & Tewksbury, 1997). While the concepts are often used separately, they are also sometimes used in combination to describe the topic. This requires the audience to make two connections between what they are familiar with and that with which they are unfamiliar. Each metaphorical concept draws on different personal schemas readers hold that are then used to map meaning onto the scientific complexity of gene-editing (Niebert & Gropengiesser, 2015). In addition, each concept contributes to the larger body of language used to communicate science to the public. The results of this study identified common metaphorical concepts being used in the popular press, which tells communicators how gene-editing in agriculture is being conceptualized in the media, thus shaping public understanding and opinion regarding the topic.
Phase Three
Even when controlling for confounding variables, the results of Phase Three indicated no significant differences between the control, creation metaphor, text editor metaphor, or tool metaphor mock news articles on issue-relevant thinking or willingness
153 Texas Tech University, Nellie Hill, August 2020 to share the article on social media. Even though previous research has found metaphors enhance elaboration and influence behavioral intentions, this study specifically investigating metaphors for gene-editing in agriculture did not elicit these same outcomes
(Corner & Pidgeon, 2015; Hongyan & Sixian, 2019; Johnson & Taylor, 1981).
Participants in this study may still be forming an opinion about gene-editing in agriculture, even after viewing the informative article. Gene-editing in agriculture may not be a topic relevant enough to elicit elaboration at this time because the public is still forming opinions about it and products have yet to proliferate grocery shelves (Brossard,
2018; Miller, 2004; Rainie, 2017). Therefore, participants may still be too early in the innovation-decision process to commit to share articles about the topic on their social media pages. In addition, the survey was conducted during the coronavirus outbreak and pandemic, which may be a very powerful distraction from the topic of gene-editing in agriculture even as CRISPR gene-editing technology is being discussed as a possible answer to COVID-19 testing (O’Keefe, 2016).
Although no significant differences were found, it is important to test theory and communications messages to develop understanding of public response. These results enhance communicators’ knowledge of what does and does not drive audience elaboration and desirable behavioral outcomes.
Summary
This study utilized three phases to evaluate online news and social media in terms of how, who, and in what manner information regarding gene-editing in agriculture is communicated to and among the public. The results of this study give researchers, science communicators and agricultural communicators a fuller understanding of the
154 Texas Tech University, Nellie Hill, August 2020 current conversation around the topic in terms of who is participating, what is being said, and how gene-editing in agriculture is being explained to the public. The identification of metaphorical concepts used to describe the product and process of the topic allowed for the subsequent testing of these concepts for their persuasive effects. This study indicated there is steadily increasing interest in the topic of gene-editing in agriculture, yet the most effective way of explaining the complex science of the topic remains unclear.
Recommendations
Research
Gene-editing in agriculture is a relatively novel topic in public discourse. A longitudinal study containing phases similar to this one is necessary to track and identify how public opinion changes and can be influenced as the public and the media become more familiar with the topic, and the technology evolves.
Regarding specific recommendations based on the results of Phase One, researchers should consider a longitudinal study to see how frequency of mentions, reach, engagement, and sentiment of the conversation changes over time with the advancement and prevalence of gene-editing applications in agriculture. In addition, opinion leaders and their followers should be identified to understand who is driving and participating in the conversation. For a more complete understanding of public opinion of gene-editing in agriculture, conversation about the topic could be monitored on other social media platforms, such as Instagram and Facebook, as well as through public opinion polls.
Furthermore, discussing the uncertain and complex topic of gene-editing in agriculture in
280 characters on Twitter is also limiting. Future research should identify the information beyond the tweet that garners engagement as well as an improved understanding of the
155 Texas Tech University, Nellie Hill, August 2020 science. This would assist practitioners with developing the content and choosing the appropriate content delivery methods, such as videos or articles, that best reach the public while also effectively communicate the science.
Based on the results of Phase Two, it is recommended that future research investigate how the metaphors for gene-editing in agriculture used by mass media change over time, region, and outlet as the technology develops and public understanding and opinion evolves. There may be regional differences in how the topic is discussed in metaphor and understood based upon the lived experiences common in the region. In addition, there may be differences in how agricultural versus mainstream publications decide to metaphorically explain the topic. The results of this study identified common metaphorical concepts being used in the popular press, but additional research is needed to identify the accuracy and strength of the concepts, which are often criticized in the literature (Rose et al., 2019). Finally, due to agricultural and human applications of gene- editing commonly being discussed side-by-side in the news, future research should investigate the implications of such pairing to see if this affects acceptance of plant and animal products of the technology.
Recommendations based on the results of Phase Three are to integrate more concepts of persuasion and factors affecting elaboration into the experimental design.
Future research using a similar design may want to further integrate the metaphorical concept throughout the stimuli to allow for repetition. Additional analysis of significant differences in attitude of issue-relevant thoughts between metaphorical concepts needs to be conducted. Researchers should explore the impact of metaphorical concept on other behavioral outcomes associated with elaboration to see what meaningful outcomes can
156 Texas Tech University, Nellie Hill, August 2020 arise from changing the metaphor. Future studies should test the metaphorical concepts in written and visual forms delivered via mass media as well as interpersonal communications so as to assess what differences in elaboration and behavioral outcomes there might be between the various forms of delivery.
Practice
Based on the findings of Phase One, practitioners are encouraged to monitor social media for gene-editing in agriculture content so as to ascertain who is participating in the conversation and what they are saying. This knowledge could be used to provide information to the public in meaningful ways that shape public opinion on the topic. As communicators will seek to expand the conversation about applications of gene-editing, results indicate mass media channels hold the greatest opportunity to reach more people who are seeking information about the topic. At the same time, practitioners should not overlook personal accounts, those not tied to larger organizations, that best engage followers in the persuasion stage of innovation-decision making. Regardless, messages should be crafted with an optimistic or affirmative sentiment to join and steer the conversation that is already rising in positivity.
The findings from Phase Two indicate the online news media are using metaphors to explain complex science topics so as to connect with their audience as well as develop their understanding of gene-editing in agriculture. Practitioners are encouraged to utilize accurate metaphors to describe such complex topics in ways that connect the unknown with the lived experiences of the target audience to create connection. Metaphors are a way to bridge the scientific community and the public. Special care is encouraged to bolster advancements in gene-editing through communications that clearly differentiates
157 Texas Tech University, Nellie Hill, August 2020 agricultural applications from human ones. Human applications of the technology bring up moral and ethical concerns among the public (Baumann, 2016).
Phase Three findings indicate scientists, science communicators, and agricultural communicators should continue to explore which metaphors to describe how gene-editing in agriculture works are most effective in persuading their target audiences. Conclusive direction for encouraging issue-relevant thoughts and behavioral outcomes in individuals was not found, but that does not mean metaphors are ineffective when it comes to persuading an audience.
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