“No room for thinking, under the dome”. Flat and the boundary construction between science and non-science on .

Luca Carbone, ANR: 416453, SNR: 2013251

Abstract: With few exceptions, most of human history since the sixth century B.C. has been guided by the belief that the earth is round. Nevertheless, in recent times, the view proposing a flat earth has increased in popularity. Drawing from the literature about boundaries and boundary-work from Gyerin and Abott, this study aims at exploring how the boundaries between those defending science and non-science are constructed in public space. Given that the Flat Earth Society (FES) is almost uniquely present online, the normative power of defining knowledge cannot be evaluated in its core dimensions – through argumentations in scientific journals. For this reason, the periphery of science, its connections with the public and the narratives adopted on Twitter, is the principal avenue where I study the acquisition of normative and classification power to define knowledge systems. Based on a qualitative content analysis, this paper shows that FES supporters and adversaries are heterogeneous in the argumentations held to sustain their positions, something that complicates the general view of these groups as homogeneous groups. Based on a network analysis I analyze the boundaries between supporters and adversaries of the theory that the earth is flat. The boundary between these two factions appears to be mostly defined by FES adversaries through framing strategies such as debasement and homogenization of FES supporters. The results show how communication defines social and normative boundaries between science and non-science. At the end of the paper, I discuss the relevance of these findings for theories about the constructivist nature of science. Introduction With few exceptions, most of human history since the sixth century B.C. has been guided by the belief that the earth is round (Russell, 1997). Nevertheless, in recent times, the view proposing a flat earth has gained momentum, generating hypotheses, theories, experiments, and, in the end, a global movement amounting to 72.5 thousand followers on Twitter, now called Flat Earth Society (FES). Despite the scientific consensus around the shape of the Earth, the debate on this issue is thriving in the wider society1. This raises many questions about the reasons, modalities, and mechanisms through which discrepancies between science and non-science take shape. Two quotations from FES’ supporters might give an idea of the heterogeneous positions that could be found in this type of relationship.

“Can anybody confirm what 'gravity' is please? ��� didn't think so. Keep spending tax payers money working on that explanation though ��� #wasteofmoney #flatearth #wakeup #thetruthisoutthere”

“#Atheist #Globies �: #HappySabbath �: #Research #Biblical #FlatEarth Psalms 96:10 (#KJV) Say among the heathen that the LORD reigneth: the world also shall be established that it shall not be moved: he shall judge the people righteously. #CatholicTwitter : #NASA �”

The two positions have different points of departure to sustain the same view: while the former expresses distrust toward the scientific establishment and the impossibility to make a claim without first-hand experiences, the skepticism of the second comes from a religious branch of the movement, where the claim that the earth is flat derives from the Bible. These tweets highlight two fundamental problems in the scientific literature about the discrepancy between science and non-science. The first regards the view that non-science is internally homogeneous (Harambam & Aupers, 2015, 2016), while the second remarks the neglection of questions about who holds the classificatory power to demarcate systems of knowledge (Agrawal, 1995, 2002). Regarding the first issue, Harambam and Aupers (2015, 2016) point to the limits that academic works on conspiracy theories (CT) have in conceiving them as a dysfunctional product of society, the fruit of paranoia, bad science or religious beliefs. This leads to a preemptive portrayal of CT as devoid of any agency or project, as a monolithic and homogeneous entity. Challenging this approach, the authors show not only the presence of different reasons, worldviews, and practices among the same CT, but also different ways in which their proponents define themselves, as critical

1 https://www.economist.com/graphic-detail/2017/11/28/americas-flat-earth-movement-appears-to-be-growing, https://www.sciencefocus.com/the-human-body/the-rise-of-the-flat-earthers/, https://www.newyorker.com/science/elements/looking-for-life-on-a-flat-earth 2 thinkers instead of conspiracy theorists, for example. The homogeneous depiction of non-scientific efforts by science is also detectable for FES. A quick search in Google Scholar or Web of Science shows the lack of scientific articles addressing Flat Earth Society as phenomenon of interest. When addressed, it represents a negative metaphor to ask questions like “Are we at risk of becoming members of the Flat Earth Society by continuing to use scientifically inaccurate terminology?” (Nelson, 2014, p. 190) FES becomes the archetype for everything that is inaccurate and non-scientific. With regard to the second issue, the general category of non-science constitutes the negative counterpoint of science, something that establishes a normative classificatory system. This dichotomization hides a more variegated group: not only the macro category of non-scientific disciplines encompasses a variety of subcategories, such as conspiracy theories, alternative forms of knowledge, and para-disciplines. Each subcategory is a particular milieu in itself, including and binding together different ideas and positions. Moreover, within the group of non-scientific fields, there are categories whose definition is externally attributed, such as those with the attached labels of para-scientific and alternative (to science). This makes science a benchmark against which every other form of knowledge is measured, something that can be attributed to its cultural authority (Gauchat, 2011). Does FES undergo the same normative classificatory process? In order to address this question, it should first be asked under which type of non-science it is possible to classify FES. According to their online forum, “The Flat Earth Society has dedicated itself to starting science afresh from the ground up, to begin to see the world without bias and assumption. Experiment and experience has shown that the earth is decidedly flat. Time and time again through test, trial, and experiment, it has been shown that the earth is not a whirling globe of popular credulity, but an extended plane of times immaterial.”2 Moreover, FES sustains the idea of a conspiracy promoted by “NASA, its constituents and fellow so-called ‘space agencies’; as well as those who are informed by them (including government)”3 which are blamed for actively faking space travels. The reasons for this behavior are unknown; however “financial profit is the most commonly assumed motive.”4 Another idea is that because NASA has never been able to really go “any farther than the edge of the atmosphere, [t]he earth is portrayed as round in NASA media because the general public already believes that it is round.”3 Finally, “The US Government and its European allies have a large interest in investing untold millions of dollars into hoaxing space travel because it gives a superior image to the rest of the world.”3

2 https://theflatearthsociety.org/tiki/tiki-index.php?page=HomePage 3 https://theflatearthsociety.org/tiki/tiki-index.php?page=The%20Conspiracy 4 https://theflatearthsociety.org/tiki/tiki-index.php?page=Motive%20of%20the%20Conspiracy 3 From this brief overview of how the official FES website presents itself to the online public, it is possible to categorize this movement as a against scientific institutions, justified by their involvement with political and financial powers. This is in line with the definition provided by Sustein & Vermeule (2009, p. 205), for whom conspiracy theories are “an effort to explain some event or practice by reference to the machinations of powerful people, who attempt to conceal their role (at least until their aims are accomplished).” Nevertheless, flat-earthers do not reject the scientific procedures of experiments and as tools to explain reality; rather, they bring them to their extreme consequences, for which only those able to see in the first person, know. In this sense, the Mertonian value of communism, for which scientists are asked to share their findings to the whole community, does not hold in their mentality, since it is outclassed by an extreme individualism. On the other hand, though, universalism (scientists evaluate findings according to preestablished impersonal criteria), disinterestedness (scientists do not have other motivations than the pure will of knowing), and organized skepticism (scientists do not dogmatically accept claims) are maintained and erected as lynchpins of their view. FES constitutes a peculiar example of how the distinction between science and non-science might be more subtle than generally portrayed in the scientific literature. Even if science is generally recognized for its adherence to certain values, for example, they are not necessarily its prerogative, and other entities can hold similar positions. Focusing on a specific movement, FES, instead of on a more general concept such as that of conspiracy theories, this study aims to provide more details about a specific group, delving deeper into their reasons, goals, and relationships with science. Moreover, it restricts the field of interest to online interactions, such as those happening on Twitter. In this way, it is possible to pursue the goal of self-definition, with users free to define themselves on both sides of the controversy, and that of boundary-work, the study of how certain opportunities – an online social network with 280 characters per message – are employed to bargain legitimacy on a conflictual turf, in order to determine how the boundaries between different entities are traced. To do so, the relationship between science and non- science is inserted within a constructivist perspective, with the idea that science is not only a content- based project (essentialist perspective), but also a system embedded within social projects. There are several questions that this work addresses: what are the main groups discussing FES on public avenues, such as Twitter? What arguments and strategies guide the bargaining process for the boundary between science and non-science? What is the most successful coalition in this process and why does it prevail? Is a normative classificatory system established?

Theoretical overview: FES in a field of scientific contestation Historical overview, principles, and relationship with scientific values

4 The theory of a flat earth had a scattered presence in ancient cultures (Garwood, 2007; Needham, 1986). Even if present in various societies and at different historical epochs, these ideas have never constituted the predominant perspective, maintained by the round earth position, which, since its first moves with , , and , has been continuously backed by empirical and experimental evidence (Dreyer, 1953 [1905]). Nevertheless, during the 19th century the English writer Samuel Rowbotham produced several pamphlets arguing that the "Bible, alongside our senses, supported the idea that the earth was flat and immovable and this essential truth should not be set aside for a system based solely on human conjecture." (Garwood 2007, p. 46) The religious nature of these claims were maintained also by Rowbotham’s successors until the 1956, when founded the International Flat Earth Research Society, reducing the emphasis on religious arguments and stressing the idea that photos shot by astronauts were distorted by the use of a wide-angle lens. After a small decline, the movement was resurrected in 2004 by Daniel Shenton, who founded a web- based discussion forum which is still alive and has acquired increasing consensus. As shown in the two tweets above, the movement has, since then, gathered a variegated group of supporters. In order to understand its composition and the normative classifications that are established between science and FES, a context where these positions actually interact is needed. The concepts of boundaries, entities, and boundary-work provide a vocabulary to describe this type of context.

Boundary-work: boundaries, entities, and social ground Regarding the relationship between an essentialist and a constructivist perspective, it is possible to argue that the cognitive authority granted to science mostly derives from the legitimacy acquired in public debates, rather than from its core qualities (methodologies, institutions, , consequences). The term coined by Gyerin (1995, p. 405) to define this process of authority acquisition is boundary-work, which “occurs as people contend for, legitimate, or challenge the cognitive authority of science – and the credibility, prestige, power, and material resources that attend such a privileged position.” Boundaries can be studied by looking at cultural manifestations, rather than balancing the argumentations about content, because “‘[u]nique’ features of science, qualities that distinguish it from other knowledge-producing activities, are to be found not in scientific practices and texts but in their representations.” (Gyerin 1995, p. 406) Hence, it is necessary to look at representations and narratives not only about science, (scientific divulgation and communication, Cassidy, 2006; Evans, 2009), but also about the relationships between science and non-science (Mizrachi & Shuval, 2005; Shuval & Mizrachi, 2004). This means focusing on how the defense of scientific borders is distributed

5 among different actors. For example, the actual content of conspiracy theories is never addressed in scientific publications, because conspiracy theorists never publish in scientific journals. Subsequently, scientists never address the problems raised in avenues that do not belong to the scientific community, which are left to be “debunked” by the rest of the society. Studying the process through which science defines legitimacy boundaries needs to look at the representations that the larger public provides of scientific issues. In order to have a better understanding of this argumentation, the concepts of boundary, entity, and social space have to be explained. First of all, boundaries should best be understood as the persistence and reproduction of forms of difference in a social space, a process generating entities. In this case, for example, differences in the normative power and cultural authority would be considered as boundaries when employed by actors to self-identify as scientific in order to classify others as non-scientific (and when this classification leads to positive consequences for the former and negative for the latter). In other words, “social entities come into existence when social actors tie social boundaries together in certain ways. Boundaries come first, then entities.” (Abbott, 1995a, p. 860; see also Abbott, 1995b) This means that science and non-science do not exist prior to their conflicts in the establishment of boundaries; rather, certain differences (values, methods, instruments, funds) are persistently yoked together in order to create a boundary which defines entities. Definitory power is the ability to delimit the boundaries that classify entities. This processual perspective permits the study of the emergence of science and non-science without considering them preexisting before any conflictual bargaining. Secondly, an entity is a combination of qualities that become internally coherent (i.e. the realization of one quality does not hinder the realization of the others) once aggregated under a recognizable label. Science, for example, is described by its epistemology (Popper, 1959), values (Merton, 1973), and fluctuating progress (Kuhn, 1962). Conspiracy theories maintain some of these characteristics (as shown above for FES), with the addition of solipsism and skepticism. These entities do not emerge prior to any form of relationship with other entities: they exist only in ecologies. Abbott’s (1988) approach to the study of professions paved the way for an ecological approach to knowledge systems, in competition with each other for the acquisition of legitimacy over certain issues. Boundaries are created through the appropriation of legitimacy claims, and the resulting entities define a certain set of qualities according to where the boundary lies. When these qualities become internally coherent and cohesive systems of beliefs, it is possible to define them as entities (Gyerin, 1983). Science and non-science can be considered as entities only when inserted within a conflictual field for the establishment of legitimacy boundaries. The third concept is that of social ground, otherwise defined as social space or field (Bourdieu, 1985; Abbott 1995a,b). According to classical electromagnetism theory, one of the most important

6 characteristics of fields is to “explain changes in the states of some elements.” (Martin, 2003, p. 4) In Bourdieusian terminology, the field consists of opportunity structures (rules of the field), in which actors (or entities) with certain positions (habitus and capital) take advantage of the opportunities offered by the field itself. Hence, the interaction between field structure and actors’ positions is what permits change. Conceived in this way, the process of boundary formation between science and non- science cannot prescind from considerations regarding the field in which it takes place. As presented above, when talking about the social dimensions of science, boundaries among these two entities cannot emerge in academic publications (because, by definition, non-scientific positions hardly access core areas), nor in institutional settings such as universities. These boundaries emerge in social settings where communication is open to everyone, such as on social networks. Twitter, in particular, has three main characteristics defining its rules as a field: the limitation of 280 characters per tweet, forcing users to express ideas with symbolically dense utterances, hashtags, and visual contents (e.g. videos, images, GIFs); the publicity of users’ profile information and timelines, for which everyone can see everything posted by other users; the openness for everyone to create a free profile and interact with other users. Besides these, there is a multitude of rules, more or less explicit, that guide the interaction between users, with the possibility of posting tweets and comments as expressions of personal thoughts, and retweets and likes to share and/or support others’ thoughts. In other words, Twitter is a particularly suitable field to study how entities bargain their boundaries through symbolically dense utterances, narratives and rhetorical styles (Geertz, 1973), in order to acquire legitimacy over certain areas. In conclusion, it could be said that boundaries are the final products of a process, boundary-work, which has the main function of dividing and defining entities. This conceptualization ensures that the presence of boundaries is not taken for granted, nor that of entities; rather, the stress is put on the process through which they are formed.

Boundary-work in action: structure of the discourse A constructivist approach conceives entities as the product of a conflictual relationship meant to establish normative and legitimacy boundaries, and fought on many fields with their own specific rules. Those governing Twitter are discursive. That is, given its symbolically dense and interactive environment, users’ behavior is guided by discursive choices (e.g. what type of words to use, what is the narrative behind hashtags). As mentioned in Leifeld & Haunss (2012, p. 383) “[t]he structure of the discourse constrains the set of feasible actions by political actors” and, because these actions constitute the bricks for the construction of a boundary, it is essential to analyze the properties of and the relationships between the entities emerging from discursive choices.

7 Binding this vocabulary with the one previously presented, the first step where entities with certain qualities take shape according to the rules of the field involves the articulation of their core content: in this situation, boundaries are created in the minds but not yet in practice. When conceived at a prenatal status, as differences in certain defining characteristics, they are not articulated to be put in practice; they are prescriptive. That is, they diagnose and identify a common problem (in this case, the shape of the earth), providing different solutions for it. Being put in practice means performing discursive strategies meant to acquire legitimacy (i.e. boundary-work), a process delimiting internally coherent systems, namely entities. Abbott’s dictum “[b]oundaries come first, then entities” (1995a, p. 860) should be better contextualized in its processual nature. Because boundaries do not come in a vacuum but in a field, and the field is created by preexisting entities with certain qualities, those stemming from the process of boundary-work cannot be compared with those before the process, even if the former derive from the latter. What distinguishes these two types of entities is their communicative enactment. In this process, a second step considers the performance of core contents among the public. In order to reach this step, where boundaries are formed and entities take shape, it should be explained how the conflict unfolds, that is how prescriptions are communicated and how they interact with other prescriptive guidelines. In other words, conflict and framing strategies become the object of study: who dominates the discussion and how? According to Leifeld and Haunss (2012, p. 385), “the dominant coalition will appear more prominently in the news media, gain a larger constituency, and it will be able to integrate the core frames into a more consistent storyline than its opponents. […] These frame alignment processes […] can only succeed if the members of a discourse coalition maintain a high level of congruence […]. In social network terms, the dominant discourse coalition should exhibit more clustering and a higher density on the ideational congruence relation.” In other words, the expectations for a dominant coalition are to be internally coherent and externally adversary, to be capable of integrating other argumentations and of defining, in this way, legitimacy boundaries. While conceived, pre-natal entities are prescriptive and negotiable (given their abstract nature, not yet in practice); when performed, entities stiffen, becoming exclusionary and exclusive.

Methods and Measures Data collection This study uses Twitter data fetched according to specific keywords that contains the binomial “flat earth”, namely #flatearth, #flatEarth, #FlatEarth, #flatearthsociety, #FlatEarthSociety, #FlatEarthers, #flat-Earthers. The data is collected using the packages ‘rtweet’ and ‘twitteR’ from the software R (version 1.1.383). It is only possible to collect a maximum of 18,000 tweets per

8 keyword published in the week previous to the collection day. I fetched the tweets on January the 15th 2019, and the final dataset consists of 7138 unique tweets from 4396 different users (3551 for flatearth, 3552 for flatEarth, 3532 for FlatEarth, 191 for flatearthsociety, 191 for FlatEarthSociety, 3975 for Flat Earthers, 3931 for flat-Earthers).

Content analysis: method and coding schema In order to have fine-grained details about the substance of the tweets, content analysis permits a systematic interpretative approach based on a coding schema. A qualitative analysis of the tweets has two main goals. On the one hand, it aims at constructing a typology of arguments in the FES debate employing the main argumentations through which certain views are articulated and supported. This will permit the construction of a dataset to perform the subsequent social network analysis. On the other hand, content analysis aims to find the framing strategies of each position. Given the lack of previous coding strategies developed for the relationship between science and non-science, this study adopts a conventional or inductive approach (Hsieh & Shannon, 2005), that is the development of a coding schema based on the reading of a sample of tweets. The main advantage of this approach is that it avoids previous theories to influence the choice of the categories of interest. In this study, the development of a coding schema is based on a random sample of 350 tweets, consisting of 5% of the total population. After this step, a second sample of 700 tweets (~10% of the whole population of tweets), different from the previous one, will be employed to conduct the main analysis and to define coalitions and arguments. This amount is chosen taking into account a trade-off among two elements: feasibility of the analysis with one coder and representativeness among the whole population. The main analysis will be carried out looking not only at the text of each tweet but also at the context in which it has been posted (e.g. as a comment to other tweets), as well as at the user who has posted it, in order to discern intentions of ambiguous tweets. The dimensions emerging from the first sample of 350 tweets, and presented in table 1, are four: • Tweets refers to the posted messages. The two characteristics are tendency (supporting or contesting FES), and type (sent by private user, public page or bot). Public pages are defined as pages, not single users, with more than 1000 followers. Bots are fake users, programmed to do an action such as writing or retweeting a post at scheduled times. • Argumentations are the reasons used to sustain a position (core) and the strategies employed to promote them (periphery). In this macro-category each sub-category has as a possible yes/no answer (excluded objectivism/solipsism), with yes expressing explicit acceptance of

9 that category and no explicit denial. This permits the detection of apparently paradoxical behaviors (for example, the use of scientific demonstration expressing, at the same time, distrust toward scientific institutions) and a more fine-grained definition of groups. These arguments will be used in the network analysis and, together with tweet tendency, allow the detection of coalitions. • Tone refers to the communicative nuances that are meant to convey a message in a certain way. Most of them are negative, such as mocking, angry, and bothered, one is neutral, and only one is positive (i.e. support), indicating the presence of highly conflictual relationships. • Attitudes gathers the ways in which the topic is approached and constitutes a category of clues to interpret how a tweet is constructed. Given the communicative field where tweets are shared, attitudes and tone constitute the performative strategies through which arguments are constructed and concepts become bricks for the erection of boundaries.

10

Table 1: Coding schema

Main categories Subcategories 1 Subcategories 2 Subcategories 3 Pro FES Against FES Neutral (when talks about Tendency FES without a position) Uncategorized (when does Tweets not talk about FES but uses # or @) Private user Public/verified user (e.g. Type BuzzFeed) Bot Scientific demonstration (e.g. scientific, photos/videos) Coalitions Religion (e.g. the Bible as source) Core (i.e. epistemological Objectivism vs. solipsism assumptions) (e.g. truth as right and evident vs. truth as found individually) Frames/argumentation Scientific values (e.g. observable, testable, repeatable) Scientific trust Scam (e.g. use of Periphery (i.e. strategies to photoshop) weaken or reinforce one Paradox (e.g. FES using side) scientific methods against science)

Neutral

Of the method (e.g. YouTube) Extreme and absurd examples (e.g. “if having convictions means being a Mocking good person, FES are the Tone best”) Issue a challenge (e.g. “let’s try to go to the edge of earth”) Angry (e.g. insults) Bothered (e.g. “impossible

to reason with them”) Performative framing Supportive (e.g. awakening) Mixing (e.g. types of non- science, other ideas as feminism) Ad hoc attacks and generalizations (e.g. others

as bad people, lack of education) Attitudes Need to take action (e.g.

block FES, online debunk) Connections (e.g. with

politics, with the media) Curiosity (e.g. “I am just curious to know what do you think about…”)

11 Discourse network analysis: method and dimensions The first goal of DNA is to test whether the group typology stemming from the content analysis is supported by the connections between tweets and arguments. Two are the main advantages in flanking a qualitative evaluation of the subgroups with a network framework. First of all, the layout of the network – display of nodes – depends on a force-directed graph algorithm called KamadaKawai (Kamada & Kawai, 1989). This algorithm distributes nodes according to the graph theoretic distance between them – minimum path length connecting each node – and allows to say that the formation of subgroups in the network depends on the number of close relationships between nodes: the closer the nodes in the graph, the shorter the path between them, given their relationships with the rest of the network. The second advantage resides in the possibility to model the meaning of edges. As it is described below in the mathematical definition of the networks, the relationships between nodes could be of agreement or disagreement. This means that the display of the network does not only depend on the type of node but also on the type of edge. Groups are not only evaluated on the basis of their argument, but also relying on the relationships between them and the rest of the network. The network used to map subgroups of tweets is called actor-congruence network (Leifeld & Haunss 2012) and defines the links between tweets according to the arguments employed. This constitutes a measure of discursive similarity. The general idea is that the more concepts two tweets agree or disagree on, the more likely they are to belong to the same discourse coalition. It is composed by three elements: tweets (A = {a1, a2, … an}), concepts (C = {c1, c2, … cn}), and relationships (R =

{r1 for agreement, r2 for disagreement}). When tweet a1 talks about concept c1 in the same way as tweet a2, they have a relationship of agreement. When they talk about the same concept in different terms, the relationship is of disagreement. This network displays only relationships of agreement and is described by the following graph:

� = (�, �) ���ℎ �(�, � ) = � (�) ∩ � (� ) (1)

� (�) refers to “the set of neighbors of vertex � – that is, the set of concepts to which the actor refers” (Leifeld & Haunss 2012, p. 392), while � represents a tweet that is different from �. The second goal of DNA is the presentation of how the conflict for the definition of a boundary unfolds. Two are the networks that could be employed to reach this goal. The first network is called conflict network. A conflict develops when two tweets have a relationship of disagreement. This network is represented by the following graph:

12 � = (�, �) ���ℎ �(�, � ) = � (�) ∩ � (� ) + � (�) ∩ � (� ) (2)

A second type of network is defined as concept-congruence network. The basic idea is that two concepts are connected if used by the same actor in the same way, representing a measure of storyline coherence. This network is represented by the following graph:

� = (�, �) ���ℎ �(�, � ) = � (�) ∩ � (� ) (3) with � representing a concept different from �. These networks (synthetically represented in figure 1) help studying how variegated and yet coherent (eventually) are the argumentations put forward by each group as well as the domination of one coalition over the other. The operationalization of the concepts analyzed in DNA is:

• Internal coherency: modularity in actor-congruence network; • External adversary: modularity in conflict-network; • Ideational congruence: within-group variety of arguments in actor-congruence network (groups argumentative heterogeneity); • Numerical domination of the network: “number of vertices per coalition in the actor- congruence network” (Leifeld & Haunss 2017, p. 386); • Argumentative domination of the network: degree centrality in concept-congruence network.

Figure 1: schematic visualization of the different networks of interest.

13 Analysis Content analysis One of the main assumptions of this study is that non-scientific positions are more articulated than what is generally portrayed by the scientific community. For this reason, allowing people to self- define their positions is not only accurate but also desirable. Hence, the first inquiry in the content analysis regards the composition of the milieu discussing FES. In the sample of 700 tweets, out of 579 unique users, 95 tweets express support for FES, 302 are adversaries, 120 are neutral, and 62 are uncategorized (among this group, some of the accounts were found to be suspended because violating some of Twitter’s rules5). If all the observations were taken into account (hence considering that some users have written more than one post), there would be 148 supporters, 346 adversaries, 131 neutral, and 75 uncategorized. Among the supporters, only 20 users have more than one tweet, with two users particularly active with 16 and 10 tweets; the adversaries are less consistent, with 17 users with more than one tweet and one with 19 tweets. These numbers give a first idea about the posting behaviors of this population: both the supporters and the adversaries of FES are groups formed by a small core of users with multiple tweets and a vast periphery of occasional users expressing comments and opinions about this topic. On the contrary, there are few users who tweet in a neutral or uncategorized way more than once. In the posting behavior of this sample, another descriptive summary important to notice is the type of users in each group. While most of the users are private, very few are public. More interestingly, more than 14% are bots among the supporters of FES (and each of these bots have generally more than one or two tweets), while adversaries have less than 1%. The standard tweet from a bot looks like the following:

“#NASA are LIARS! 100% #FLATEARTH PROOF HERE: https://t.co/l8SpsYYJVB Wednesday January 9 2019:12:42:22 PM”

This shows one of the possible ways in which the community of supporters is built, scheduling tweets that continuously reinforce a certain message, defining the enemy, and providing solutions. Looking at the reactions to these messages, though, they receive a very scarce attention to the general public, with scattered likes and few retweets. Having analyzed these elements, it should be clarified that this work does not focus on users; rather, tweets are the discursive elements taken into account, because it is here that positions are articulated and performed. For this reason, the labels “supporters”, “adversaries”, “neutral”, and “uncategorized” refer to tweets, not users. It should also be stressed that

5 https://help.twitter.com/en/rules-and-policies/twitter-rules 14 tweets opposed to FES (i.e. adversaries) are not written by scientists. It might be possible that positions held by these tweets are not in line with those that would be adopted by scientists. This is an important point to highlight the constructivist nature of science. The dimensions considered important to define something as scientific (such as scientific trust or values) show how the public makes sense of what science is and how to support it. A second element in the definition of a typology refers to the argumentations put forward to support certain positions. To do so, figure 2 provides the percentages of classification for each tweet, divided in four groups according to their position toward FES. The following typology is constructed taking into account the results shown in figure 2 as well as the ideas emerged during the online search.

Figure 2: percentages after coding for supporters and adversaries of FES. Each row sums up to 100%, with light grey bars representing missing values.

Among the supporters, it is possible to distinguish three main groups. The first can be labelled conspiracy, and gathers those tweets where it is expressed the belief in a conspiracy, where scientific institutes or scientists are seen as the main actors behind it (hence, with no scientific trust), and that

15 employ as main argumentation a solipsistic perspective, for which only those who can see for themselves know. Besides those posted by bots, a typical tweet of this group looks like the following:

“NASA’s crap science falls apart as soon as someone with a brain, who isn’t invested in the Academic Mind Control System, actually looks deep into it. #truth #flatearth”

There could be various facets of this group. The one shown above represents the harshest, with only few users adhering to each principle of the group (distrust against science, solipsism, and scam). Others can be considered, even if with milder positions (for example viewing a scam guided by the scientific establishment but without a solipsistic solution). Summing up the tweets expressing adherence to at least two of these three pillars, this group constitutes 27% of the supporters. Among the supporters, a second group is composed by those who express closeness to scientific values (such as reproducibility and experimental design) and who put forward scientific argumentations to back their claims. This group can be labelled scientific and expresses opposite views compared to conspiracy about the epistemological source of knowledge (objectivism or solipsism). A point should be stressed about this label: the validity of a scientific claim cannot be evaluated on the basis of single affirmations. This means that claiming the scientificity of an utterance does not immediately give those words a scientific status. A sociological attention toward the understanding of people’s behaviors and intentions cannot dispute the validity of these claims: it is, and should be by its own very nature, impartial. Hence, the labelling of this group as scientific does not want to provide or grant scientific validity to the claims put forward by its members. Rather, this label depicts the ways in which this group frames its own arguments. One of their typical tweets is the following:

“It's not just a few crazies on the internet. There are literally millions of flat earthers. This is a huge misconception that it is just a few people who believe this or are doing tests. Tests are being done on a daily basis. Real world tests not someone sitting at a desk.”

As can be noticed, the conspiratorial vein present in conspiracy as mistrust toward science is substituted by a more general critique against “[those who make tests] sitting at a desk”. Moreover, the solution is not seeked in a personal search, but more through experiments and tests. As previously done, summing up users who express adherence to at least two of the three pillars, the scientific group constitutes 18% of the supporters.

16 A third group represents religious argumentations put forward to sustain the flatness of the earth. Considering religion as the only category defining this group, it represents roughly 14% of the supporters of FES. A typical tweet is the one shown at the beginning of this paper, which mostly refers to the Bible as source of truth. Six members of this group (out of twenty) also express distrust toward science, while none thinks that a scam is enacted by members of the scientific community. Considering the evolution of this movement, it is interesting to notice that even if the movement founded in 1956 by Samuel Shenton aimed at reducing the emphasis on religious arguments, this branch is still active. Nevertheless, the emphasis has convincingly shifted toward a more conspiratorial tendency. For what concerns FES opponents, two groups can be discerned. The first assembles tweets that actively counteract FES claims through scientific demonstrations, and can be labelled debunkers. A typical argument from this group is the following:

“Why do flat earthers always thin[k] that our planet is the size of a small round object AND have absolutely no understanding of gravity???? This insane argument drives me mad. Research gravity!!!”

Summing up those tweets that express adherence to at least three of the four pillars of this scientific group (objective epistemology, scientific values, scientific trust, and provision of scientific demonstration), it constitutes less than 12% of FES adversaries. A second group among adversaries can be defined skeptical and is composed by those who, instead of trying to argue against the claims of the supporters, try to weaken their credibility, highlighting the paradox and the scam leading FES. One of the most frequent examples of this group is:

“Flat Earthers plan a cruise that uses charts of a round planet to navigate.”

This group sums up to 23% of the adversaries. Among them, only 11 are also debunkers, which means that most does not have the provision of scientific explanations as main scope; rather, they aim at disclosing paradoxes in order to debase the credibility of the source. One of the reasons for the study of this phenomenon on Twitter was the impermeability of the scientific community to FES arguments. In other words, in no scientific journal it is possible to find papers challenging FES views. Hence, the onus to face the challenge is left to the general public, and Twitter is one of the avenues where confrontations about this issue happens. This analysis has raised

17 two important points in this respect. First of all, there is one instance where a former university professor is cited for having conducted studies defined as scientific to test some of FES hypotheses. These works are published on an online Christian journal (with no metrics of comparison with scientific journals, such as the impact factor) and disprove FES claims. Even if it is a single case, it might represent an interesting situation of bargaining between the scientific and the non-scientific, with the creation of alternative sites of self-defined scientific discussion. Given the religious basis of some of FES adherents, the distincion between faith and science is, in this way, blurred. Secondly, the emphasis put on the role of the online public in “debunking” FES claims creates expectations about the magnitude of this activity: since they are the only frontline facing FES, it is expected to be a compact and large component. Contrary to this expectation, less than 12% of adversaries try to actively debunk FES, while skeptics are more numerous. Compared to the magnitude of the arguments put forward by FES supporters, this is a rather small group. These findings represent a first disrupture in the general attitudes toward FES, which can be generalized to those toward conspiracy theories (Melley, 2000, Fassin, 2011) and linked to the findings of Harambam and Aupers (2015, 2016). Not only FES supporters show internal fragmentation, but also adversaries employ different argumentations which lead to two main frontlines against FES. The results proposed above constitute an important step in the definition of boundaries, a process still in-fieri at this phase. These groups, both among supporters and adversaries, are not delimited by clear boundaries, and, for this reason, are not considerable as entities yet. They are classified according to principles, that is following main lines of argumentations. Most importantly, since boundaries should be considered as articulations of forms of difference, the groups instantiate prescriptions and represent differences, not boundaries. As the overlap of principles between scientific and debunkers shows, when looking at the argumentations put forward by these groups, the main difference relies on the group that is defined as the opponent. In order to evaluate how these principles are enacted, showing in this way the construction of boundaries and the formation of entities, a second step regards the performative ways in which these positions are communicated through framing strategies. At the aggregate level, it is worth noticing that while supporters of FES concentrate more on articulating various types of argumentation (stacked bars in figure 2), adversaries are more active in performative strategies (black bars), which means that they tend to express contents more often through framing rather than argumentations (for example, supporters have 15% of neutral tweets in emotional terms, while adversaries 10% – hence less neutral posts). This is an important measure, especially when flanked by the comparison of the magnitudes for each group. Adversaries are more than twice the supporters in numbers, which means that there are more tweets arguing against FES rather than in its support. In parallel, supporters

18 employs more argumentations, while adversaries concentrate more on the framing strategies. In other words, adversaries are more active in the construction of a boundary, both in magnitude and in practice. Looking more specifically at the types of arguments used to convey certain principles, the top-left quadrant in figure 2 shows that FES supporters are very supportive of their own community, creating a strong sense of ingroup through hashtags like #WeAreWakinUp or through tweets galvanizing the group with encouraging lines:

“Stand up against the controlled opposition. Don't be scared to speak up.”

On the other hand, FES adversaries are more prone to create an outgroup, tweeting with more mocking, challenging, angry, and bothered tones, where insults are mixed with explicit debasements of others’ arguments. This debasement is often times the only content of the message, without an argumentation put forward to sustain the expression of certain feelings. Another information that can be gathered by looking at the framing strategies is the mixing behavior of the adversaries. A typical way adopted to mock and debase FES is to mix its supporters with other social groups which are – implicitly or explicitly – considered as negative examples. A typical tweet in this case is:

“The stupidest people in US society today:1. MAGAts; 2. Anti-vaxxers; 3. "Carnivores" (meat-only diet); 4. Flat-earthers. All convinced that they're the smart ones. Relax. There is no Deep State / Big Pharma / Big Pasta / NASA conspiracy against you ... You aren't that important.”

This behavior becomes even more interesting when looking at how much FES supporters tweet about connections with any of these realms. The category Connection expresses this idea and, as could be seen in figure 2, the percentages between supporters and adversaries are the same (both very low). Besides tweets claiming the deception carried out by NASA, the articulation of who has evil connections with whom is almost never employed. This could represent a framing style for the adversaries based upon the creation of an outgroup: heuristically, flat-earthers are depicted as an homogeneous group, as dangerous as other homogeneous groups such as anti-vaxers or Trump supporters. This idea is also supported by the amount of ad-hoc attacks (e.g. “I blame our education system. How else do you explain so many flat earthers?”) made by FES adversaries, where certain features (e.g. lack of education) are generalized to the whole group.

19 Is there any normative categorization which is constructed through these framing strategies? FES adversaries talk about possible actions to solve the problem represented (for them) by FES, defined by the category Action. The first solution considers (the lack of) education as the main cause for the rise of FES ideas. Among them, the direct link to school education might be more or less explicit; what is common is the need to engage in a salvation project, “to convert the unconvertable or convince flat earthers the world is round anyway”. Some have launched hashtags as “#educate him before it's too late” in order to highlight the gravity of the situation, pointing to education as the final solution. A second stream of Action tweets is more pessimistic, providing a distanciation from FES through active disengagement:

“Leave me alone. How to reason with flat earthers — even though it may not work”

This position about desirable attitudes has the implicit effect of wiping out any possible form of communication. In some cases, FES is described as psuedo-science (“I invite all of the flat earthers to form a line and tweet at me about their flat earth psuedo-science, so I can commence blocking them. I want to get that out of the way”). These two streams represent the underlying motives guiding the proposal of argumentations and framing strategies. For FES adversaries, scientific education ought to be present in order to avoid pseudo-science. In this way, science assumes an undisputed status, something which is typical in cases where boundaries define entities. What has emerged in this section is that framing strategies are mostly employed for the overt creation of ingroups (within supporters) and outgroups (adversaries debasing supporters). Certain fault-lines take shape between those with a scientific education and those without, through moralizing and normative frames. FES is actively debased and homogenized, while normative prescriptions suggest to interrupt any form of comunication with FES supporters or to engage in actions of conversion. In this situation, forms of difference (in principles) are arranged in such a way to define social (who is in, who is out) and normative boundaries (who should be in, who should be out).

Discourse network analysis The first goal of DNA is the evaluation of the typology defined in the content analysis. Figure 3 shows the actor-congruence network that is employed for the detection of subgroups. If two tweets (nodes) talk about the same argument in the same way (e.g. both use a scientific demonstration), they are connected. Subgroups are agglomerations of tweets containing the same topic and the same position on that topic.

20

Figure 3: Actor-congruence network. Labels mirror those attributed in the content analysis.6

Labelling tweets according to the qualitative typology, what could be immediately seen in this network is that nodes agglomerate very similarly compared to their labels and their position toward FES (colors). There is a clear divide between the lower area of the network (red dots), where FES supporters are conspiratorial and religious, and the upper area (orange dots), where FES adversaries are debunkers and skeptics. A closer inspection reveals two areas which are not in line with this typology. The first regards scientific supporters, a group mostly scattered. The main reason for this fragmentation is the skeptical attitude shared with skeptical adversaries or conspiratorial supporters. At the center-left of the network it is possible to detect three main groups of scientific supporters. While those at the top share argumentations with skeptics, those at the bottom are more in line with skeptics and conspiracists. In between these two subgroups, the central one is at the very opposite of debunker adversaries. When

6 See the Appendix for the number of relationships (coalitional and conflictual) between supporters and adversaries, divided by topic. 21 comparing the texts between scientific supporters in the middle and debunker adversaries, it emerges that while the former are more inclined to put forward scientific argumentations (e.g. “Days are slowly getting longer (in North America) because the Sun is travelling northward, away from the Tropic of Capricorn, toward the Equator. Spring Equinox inbound in a few weeks. Research #FlatEarth”), the latter are more concentrated in debasing or ridiculing FES supporters, also through scientific argumentations (e.g. “19 Of The Absolute Dumbest Things Flat Earthers Have Actually Said”). This division at the core of the network could be interpreted as a significant fault-line between adversaries and supporters. When it comes to argumentative similarities used to sustain a certain position, adversaries and supporters split in two main subgroups, those in the upper and lower area of figure 3. Within the core of this network, where most of the connections are established, scientific supporters and debunker adversaries are at the very opposite. Despite their similarities (both use scientific demonstrations, promote scientific values, and employ an objective epistemology), the formers are closer to conspiratorial supporters, due to their common distrust against the scientific community. On the contrary, debunker adversaries are more focused in debasing supporters’ credibility. A second aspect regards the division of skeptical adversaries at the opposite sides of the upper/lower division line. In the top-left area of the network, skeptics share arguments with debunkers, while at the bottom-right another group of skeptics is incorporated among religious and conspiratorial supporters. Multiple different configurations confirm the presence of this division, ruling out the hypothesis of a visualization artifact. To better understand the differences between these two groups, it is important to review the content of their messages. The bottom-right group is actually closer to the argumentations put forward by FES supporters: even though they explicitly reject FES, the argumentations are closer to those of their adversaries rather than those of their allies. In other words, both conspiracists and skeptics think about machinations and scams; they differ according to the group they target. On the contrary, those belonging to the top-left skeptic group are more prone to highlight paradoxes intrinsic to FES. Hence, the main difference among this group regards the strategy employed to debase supporters: while those to the left talk more about FES as a scam, those to the right highlights their paradoxes. What emerges from this analysis is an overall confirmation of the qualitative typology, with deeper insights about the relationships between groups. Most of the discussion involves scientific argumentations, a core surrounded by clouds of other argumentations. It is clear that FES supporters are not a homogeneous whole, an additional support to Harambam & Aupers’ (2015, 2016) thesis about the heterogeneity of non-scientific fields, and that adversaries mainly employ scientific argumentations to debase FES claims through skepticism.

22 In order to evaluate this claim, the concept of modularity is employed. Modularity (Q) measures the degree to which a network is partitioned in clusters, and it goes from 0 to 1: “if the number of within-community edges is no better than random, we will get Q=0. Values approaching Q=1, which is the maximum, indicate networks with strong community structure” (Newman & Girvan, 2004, p. 7). In the actor-congruence network, the modularity for the subnetwork of supporters is Qs=0.31, while adversaries has a Qa=0.20. According to Newman & Girvan (2004), the typical range of a network with a community structure is between 0.3 and 0.7, which means that supporters show a certain degree of clusterization while adversaries do not. Nevertheless, in order to interpret Q’s magnitude, a null model is needed, that is a graph with some properties similar to those of the original network, but without a community structure. The evaluation is performed comparing an element of modularity present in both the original and the simulated networks, namely edge density. Defined as the ratio between actual and possible connections (range from 0 to 1), this concept is based on the idea that “the possible existence of clusters is revealed by the comparison between the actual density of edges in a subgraph and the density one would expect to have in the subgraph if the vertices of the graph were attached regardless of community structure.” (Fortunato, 2010, p. 89) A null model is defined by the Erdős–Rényi random graph model. This model selects one of the possible networks with n nodes and M edges with a probability p of connecting the nodes, independent from the probability of drawing the other edges. I follow Rubinov & Sporns (2011) in generating 100 random networks with the same number of nodes, the same probability of drawing a link between nodes, the same strength and degree centrality distribution of the nodes in the two subgraphs. The comparisons between the actual and the simulated – average among the 100, between brackets – subnetwork densities (D) are the following: Ds=0.15 (0.008), Da=0.37 (0.003). As expected, the simulated networks have a very low edge density, they are sparsely connected. On the contrary, the two subnetworks are more densely connected, with the density for adversaries more than twice that for supporters. The information provided above, Qs=0.31, Ds=0.15;

Qa=0.20, Da=0.37 shows that supporters have a sparsely connected (low density) clustered structure, while adversaries have a densely connected, non-clustered structure. These results show that adversaries have a higher level of agreement within themselves, while supporters are more fragmented. Looking at figure 3, it could be said that a first line of differentiation about core arguments divides supporters and adversaries when adopting scientific argumentations. However, this network does not show the relationships of disagreement between tweets, which are fundamental in evaluating how much tweets interact not only positively, constructing clusters, but also negatively, defining differences.

23 A second step in DNA regards the evaluation of these conflictual relationships. At this stage, it is possible to map the creation of boundaries looking at the conflict between tweets, as well as at network’s characteristics such as clusterization of arguments and argumentative coherence. Figure 4 shows the conflictual network. This network employs tweets as nodes, while edges are formed as relationships between pairs of tweets and arguments, reversing what it is done in the actor- congruence network: when two tweets talk about a concept in the opposite way, they have a connection (for example, one supporting scientific values, the other denying them). Tweets cluster when they have many conflictual relationships with common targets (not between themselves).

Figure 4: Conflict-network. Labels mirror typology from content analysis.

This network shows a higher level of fragmentation than previously presented in figure 3. While religious tweets are absent from this graph (hence, no conflict about religious positions), conspiracists supporters enter the core, forming a fault-line against debunker and skeptical adversaries. This fault-line indicates that boundaries are constructed while using scientific argumentations and mainly through reciprocal skepticism about the source of the argumentation. Another information that should be considered is the modularity of the subnetworks in figure 4, which is a measure of how much nodes cluster according to common enemies. As previously done for the actor-congruence network, the comparison between modularity and density among the

24 two subgraphs of adversaries and supporters is (simulated networks between brackets): Qs=0.29,

Ds=0.16 (0.007); Qa=0.25, Da=0.16 (0.003). These values confirm some characteristics present in the previous network: while supporters show some form of clusterization in a scarcely dense network, adversaries are not clustered. In this case, though, it is interesting to notice that the density for adversaries is lower, indicating a low level of conflict within themselves. This can be interpreted as sign of entities formation: when performed, entities stiffen, becoming exclusionary and exclusive (high agreement and low conflict within adversaries, low conflict within supporters). The last step in the evaluation of the argumentative process of boundary formation regards the relationships between arguments. Figure 5 shows the degree centrality of the possible positions about each argument: the more central the argument, the more often it is cited. The measure of centrality shown is indegree centrality, which indicates the number of times each argument is used. In this case, values are rescaled such that the centrality scores sum to 1.

Figure 5: Degree centrality for concept-congruence networks. Black labels represent concepts with a centrality score higher than 0.12.

25 While the most central concepts for supporters are lack of scientific trust, the suspicion of a scam and a solipsistic view, adversaries privilege the use of scientific argumentations, of expression of scientific trust, values, and of an objective view. When looking at the total network, it is clear that the most central concepts are those used by adversaries (scientific demonstration, values, trust, and objective epistemology), while only scam is a central term for supporters. As already shown in the previous networks, this is a confirmation of the turf upon which FES supporters and adversaries struggle in defining what is science and what is non-science: a scientific turf. This emerges also looking at the position, in the total network, of the argumentations that could have been other possible conflictual turfs: religion and open rejection of scientific demonstration are at the outskirt of figure 5, showing a very low usage; solipsism is right next to them, indicating the prevalence in believing that reality is something that can be objectively studied instead of individually detected when it comes to the shape of the earth. As already mentioned, the network discussing FES is dominated by adversaries, both numerically and communicatively (creation of social and normative out-groups, active fronts of conflicts and coalitions). Figure 5 confirms the role played by adversaries in the creation of a boundary: they dominate the discursive network with arguments that are the most important in the whole network.

Discussion This study offers three important contributions: 1) it constructs a typology with which to map the online discussion about FES, 2) providing additional evidence for Harambam and Aupers’ (2015, 2016) claim about the heterogeneity of non-scientific domains, and 3) it provides theoretical and empirical tools with which to study how boundaries between knowledge systems are publicly constructed. To do so, a constructivist approach divides the various phases in which boundaries take shape. In evaluating the arguments discussing FES, pre-natal entities are formed through the gathering and stressing of certain differences, leading to five different groups discussing FES. This approach pays close attention to these arguments, studying how they are performed. The framing strategies and communicative devices (e.g. hashtags, jargon) are the clues that should be looked for in order to detect how boundaries are constructed, and what is defined as entities. FES adversaries are those most actively involved in the aggregation of differences defining a boundary with supporters. They constitute a dominant coalition in magnitude (in the sample, they are more than twice the supporters), argumentations (the typical arguments for adversaries are the most central), and framing (they are active in the construction of outgroups, building social boundaries, and in the

26 promotion of strategies with which to solve the “problem of FES”, building normative boundaries). Moreover, this domination appears also through the use of normative classifications, using the labels of pseudo-science and conspiracy theory to define FES supporters, as well as the frequent mixing with (explicitly or implicitly) other negative examples of social groups (e.g. Trump or anti-vax supporters). This homogeneous picture does not find realization on the other side, with FES supporters more variegated than what depicted. When the conflictual relationships between these groups are compared to their coalitional ones, a boundary takes shape along the line defining scientific argumentations. In this comparison, adversaries become more densely connected against supporters, while supporters become more fragmented. From this process, science and its public emerge as an entity with the classificatory power to define FES as a pseudo-science, a mix of positions that can be homogenized under few labels, to educate and redeem. In studying how prenatal entities become entities, this paper has the main limit of postulating a process without using a longitudinal design. Tracing users’ behaviors over time permits to identify if and how users that mainly stress differences – pre-natal entities using argumentations – become entities after prolonged interactions with their adversaries. The differentiation between different phases is here conceived in the strategies adopted by users rather than in the evolution of their positions, and future studies might take this suggestion, paying specific attention to the temporal construction of boundaries. In conclusion, these findings can inform the social studies of science in two ways. First of all, the homogenizing strategy through which non-scientific types of knowledge are clustered represents a possible threat for those areas that could contribute to social improvements, or that are already doing so. The distinction between bio- and alternative-medicine is a point in case. Contrary to FES, the creation of a boundary between these two areas happens on many more fields than on . This is because the discussion about is also present in the scientific literature, and in institutional settings such as universities and hospitals (Shuval & Mizrachi 2004; Mizrachi & Shuval 2005; Mizrachi, Shuval, & Gross, 2005). Conceiving FES and alternative medicine as a homogeneous whole under the label of conspiracy theory or non-science (van Prooijen & Douglas, 2018) has the main problem of shifting some negative categories of one side to the other, reducing the credibility and eroding the possibility to thrive in a highly impermeable environment, as science is for its cultural authority. Moreover, the possibility of certain actors to determine the agenda-setting in science strongly depends on its bargaining capabilities and legitimacy vis-à-vis official authorities (Frickel, et al., 2010). The present work shows that a widespread process of agenda-setting – ideally based on core contents – might be complicated by

27 the capability of science and its supporters to dominate communicative spaces, continuously solidifying its position at the periphery. Secondly, this paper wants to stress that the study of public perceptions of science cannot prescind from the field in which they take shape. The most common method to gather this information is through surveys (Pardo & Calvo, 2002), an approach that lacks a processual and interactive lens through which to look at the emergence of certain attitudes. A closer attention to online environments, as well as to the interactions between science and other systems, directly tackles this problem, and should provide a more solid basis for the interpretation of survey results. Not being aware of the process through which people form opinions about science might lead to the proposal of certain measures that reveal themselves counterproductive. For example, providing more scientific knowledge, as proponents of the deficit model suggest (Sturgis & Allum, 2004), overstresses the importance of core aspects of science, overlooking its periphery. As this study shows, even if science is not necessarily conflictual at its core, it often is at its periphery. This is the place where social scientists should invest more efforts detecting reasons and mechanisms through which different forms of knowledge interact and born as socially constructed.

Appendix Figure 6: Number of coalitional and conflictual relationships between supporters and adversaries divided by topic. Total represents unique relationships (i.e. if two tweets have more than one link, total counts only one).

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