#DIGITALJOURNALISM: TWITTER USE OF LOCAL
NEWSPAPERS AND TELEVISON NEWS STATIONS
A Thesis
Presented to
The Graduate Faculty of The University of Akron
In Partial Fulfillment
of the Requirements for the Degree
Master of Communication
Kelly Marie Meyer
May, 2015 #DIGITALJOURNALISM: TWITTER USE OF LOCAL
NEWSPAPERS AND TELEVISON NEWS STATIONS
Kelly Marie Meyer
Thesis
Approved: Accepted:
______Advisor Dean of the College Dr. Tang Tang Dr. Chand Midha
______Faculty Reader Interim Dean of the Graduate School Dr. Heather Walter Dr. Rex Ramsier
______Faculty Reader Date Dr. Val Pipps
______School Director Dr. Therese Lueck
ii ABSTRACT
This study examined how local television news stations and local newspapers in the United States use Twitter. Specifically, this study focused on tweet structure, news content, tweet topic, audience engagement, and promotion activity within small, medium, and large news markets. A content analysis of 4,507 tweets from 60 news organizations revealed that the size of media market, time of day, and tweet structures were main differences in Twitter activity of local television news stations and local newspapers.
Overall, this study suggests for local news organizations to continue to tweet frequently and incorporate tweet structures in order to build stronger audience engagement.
Keywords: Television News, Newspapers, Twitter, Social Media
iii AKNOWLEDGEMENTS
It would not have been possible to write this thesis without the help and support of the people around me. I would first like to thank my husband, Adam, for his love, encouragement and patience. I would also like to thank my family, especially my parents,
Tom and Donna, for their guidance and inspiration.
I would like to express my sincere gratitude to my advisor, Dr. Tang Tang, for her continuous support of my study. Her motivation and enthusiasm helped me in my time of research and writing. I could not imagine having a better advisor with such guidance and immense knowledge to mentor my studies.
I would also like to thank the rest of my thesis committee, Dr. Heather Walter and
Dr. Val Pipps for their encouragement, as well as their time and insightful comments in helping me complete my thesis.
I would like to acknowledge the support of my fellow graduate students, Fiona and Leyna. I appreciate their time and effort in helping code the data for this study.
Lastly, I would like to thank the faculty and staff in the School of Communication that were always willing to help in any capacity. I am proud to say I am a graduate of The
University of Akron because of your commitment and education.
iv TABLE OF CONTENTS
Page
LIST OF TABLES ...... vii
CHAPTER
I. INTRODUCTION ...... 1
II. LITERATURE REVIEW ...... 5
Cross Media Promotion ...... 5
News Industry ...... 8
Twitter Use ...... 11
Twitter and News ...... 15
III. METHODOLOGY ...... 23
Sample and Unit of Analysis ...... 23
Coding ...... 24
IV. RESULTS ...... 27
V. DISCUSSION ...... 35
Limitations and Future Studies ...... 41
REFERENCES ...... 44
APPENDICES ...... 51
APPENDIX A. NIELSEN LOCAL TELEVISION MARKETS ...... 52
APPENDIX B. MEDIA MARKETS AND NEWS ORGANIZATIONS ...... 57
APPENDIX C. NEWS ORGANIZATION TWEETS CODING SHEET ...... 58
v APPENDIX D. NEWS ORGANIZATION TWEETS CODING BOOK ...... 60 LIST OF TABLES
Table Page
1 Differences in Twitter Strategy between Local TV Stations and Newspapers ..... 32
2 Differences in Twitter Use between Local TV Stations and Newspapers ...... 33
vii CHAPTER I
INTRODUCTION
Tuning in to the evening news is no longer necessary with the use of smartphones and tablets. News consumers can now stay informed through news alerts and email notifications. The revolution of digital journalism has caused traditional news platforms, such as newspapers and broadcast television to be on the decline for years (Chyi, 2009).
News organizations have been forced to rethink the way news is distributed in an increasingly competitive digital environment (Holton & Lewis, 2011). As a result, newspapers and television news programs have joined social media for news consumers to have access to content on the go. If searching for news was the most important development of the last decade, sharing news may be among the most important aspects of the next (Olmstead, Mitchell, & Rosenstiel, 2011).
As the news industry continues to change, online news consumption increases.
The percentage of Americans that have seen news or news headlines on a social networking site has doubled from 9 to 19 percent since 2010 (Pew Research, 2012).
Among adults younger than 30 years old, 33 percent reported to have seen news the previous day on a social networking site, 34 percent reported to have seen any television news, and 13 percent reported to have read a newspaper in either digital or print form
(Pew Research, 2012).
1 More individuals are using social networking tools, Facebook and Twitter, as news resources (Boyle & Zuegner, 2012). Specifically, 23 percent of social network users reported they received their news from a news organization through their social network channels. Nearly a third of Internet users receive their news from friends, journalists, or news organizations they follow through social networks (Purcell et al., 2010). In a more recent study, news consumers on social networking sites were more likely than the general public to use mobile devices for news; 54 percent of those news consumers were
Twitter users (Holcomb, Gottfried, & Mitchell, 2013).
With 284 million active users and 500 million Tweets sent everyday
(Twitter.com), Twitter has proven to be an established platform in the digital landscape of journalism (Hermida, 2013). The social media’s growing presence is changing how people and news organizations exchange and consume information (Small, 2011).
According to Palser (2009), Twitter users are more likely to read a newspaper on a smartphone or website than non-Twittering Internet users. Twitter users are also more likely to have wireless connections and watch news video online and use social networks such as Facebook. Twitter is a way to reach a younger audience who is interested in certain kinds of news but do not regularly read the newspaper or news online (Palser,
2009).
With the increase of online news consumption related to Twitter, there is clearly a need for more cross media promotion studies to aid news networks and organizations to understand how to effectively use Twitter and distribute news content through social media. This kind of perspective has become a defining feature of contemporary media because it is essential to the marketing and diffusion of new media forms (Hardy, 2010).
2 Several scholars have looked at the impact of Twitter in journalism (Armstrong &
Gao, 2011; Greer & Ferguson, 2011; Bruns & Burgess, 2012; Hermida, Fletcher, Korell,
& Logan, 2012) and found that news organizations are utilizing the platform to provide news content to online audiences. However, there is very little research on how news organizations take advantage of Twitter to interact with their followers. Since local news is a dominant portion of newspapers and television news, researchers need to explore the relationship of local news organizations and audience engagement (Greer & Ferguson,
2011). Therefore, it is important to study how local news media use Twitter to disseminate news content, in order to create a stronger connection with their online audience.
Very few studies have compared the differences between Twitter use of local television news programs and local newspapers. Local newspapers may publish daily, weekly, or bi-weekly, in which information is time sensitive. On the other hand, local television airs multiple newscasts daily with updated information. Also, newspapers are written with a more in-depth approach, but television provides a visual aspect of the news. Although, the two mediums have different ways of expressing information to their audiences, Twitter is a common platform for local news media to distribute information.
Since television news and newspapers are main sources of news to the public, it will be beneficial to the news industry to know how the two platforms strategize news distribution via Twitter. There is a gap of research on the strategies that television news stations and newspapers use to disseminate content through Twitter. Thus, there is much to learn when it comes to the Twitter use of news organizations, such as the similarities or differences among Twitter activity of local media.
3 This study seeks to address the gap by conducting a content analysis of tweets composed by local television news stations and local newspapers. The study examined the relationship between news content, Twitter structure, and audience engagement, in order to learn more on how news organizations engage with their online audience. In addition, the Twitter activity of television news stations and newspapers were compared for understanding differences of promotion strategies used by print and broadcast news media in today’s convergence environment.
4 CHAPTER II
LITERATURE REVIEW
Cross Media Promotion
Cross media promotion is the promotion of one media service through another.
The term is used by marketers to refer to a promotional strategy, in which multiple media platforms are used to promote a product or service (Hardy, 2010). There are several ways media companies promote their own media products or services: media advertising, sponsorship, cross-promotion through merchandising and licensing, product placement and brand integration, advertorials, program promotions, editorial self-promotion, and editorial cross promotion (Hardy, 2010). The repackaging or repurposing of content across different media involves forms of promotion (Hardy, 2010). For example, any media outlet with a website is involved in forms of cross media promotion. “Content can reappear and be used in a variety of ways, tending towards operational alliances or integration of firms operating in traditionally separate sectors – such as print and broadcasting” (Hardy, 2010, p. 29). Thus, trends in the media industry have shown that in order to guarantee long-term success with audience, it is imperative to change from a single product to a multimedia content and user-oriented approach (Veglis, 2012).
Cross platform convergence has been one of the most discussed changes in U.S. journalism over the past decade due to technological advances (Thorton & Keith, 2009).
5 Dupagne and Garrison (2006) found that journalists viewed media convergence as a tool to produce either combined or additional newsgathering resources. They added that journalists could concentrate more on multimedia storytelling as well as increasing their knowledge of other platforms. Dailey, Demo and Spillman (2005a) also studied how newspaper-television partnerships practice convergence by surveying editors, and found that 30 percent of daily papers have partnerships with television stations. The researchers added that some partnerships reflect behaviors associated with the cooperation, content sharing or stages of convergence. In the convergence stages, cross media news organizations would plan stories based on using strengths of each media. For example, newspapers would build on the immediacy of television and the interactivity of the web
(Dailey et al., 2005a). Dailey, Demo and Spillman (2005b) also suggested a model to provide media professionals a better understanding of cross media alliances. “Cross media partnerships force individual journalists to reexamine cultural and organizational differences as they select stories, produce content across platforms, and establish news routines” (Dailey et al., 2005b, p. 166).
Bødker and Peterson (2007) studied an organization that shifted from traditional newspaper production to cross media production involving integrating digital production of newspaper, television, radio, and online news. The researchers found that production rhythms of individual media collide. They suggested giving media employees better insight and access to the daily rhythms of each media production in order to move towards cross media promotion. Thorton and Keith (2009) argued that traditional media- web relationships have become more important than collaborations between television stations and newspapers. The researchers found that about half of the responding news
6 organizations reported they had a convergence partner. However, the respondents of the collaborations reported that the partners did not work together daily or in practical manner (Thorton & Keith, 2009). Rather than turning to each other, print and broadcast news organizations were focusing on multimedia Web work. Researchers suggested that future studies should compare how television stations and newspapers are approaching multimedia separately due to broadcast-print collaborations are in decline (Thorton &
Keith, 2009). Schrøder and Larsen (2010) studied the use of the cross media news and argued that it is crucial for news providers to be able to target and distribute content on platforms where news consumption is actually taking place. The study confirmed that the role of newspapers is diminishing in the overall news landscape. The study also revealed that Internet news is now equal to television news. The significance of television news as a depth medium increases with age, the significance of Internet news media as a provider of depth is higher among younger adults (Schrøder & Larsen, 2010). These findings are critical for news producers, as it provides information on competing news media platforms. News organizations will be able to strategically target their different news platforms towards different audiences.
Greer and Ferguson (2011) used the cross media promotion perspective to look at how television stations take advantage of new media technology to promote and brand the station, as well as how stations interacted with viewers. The researchers found that commercial and public stations used the social network, Twitter, differently. Commercial stations were more likely than public to have breaking news and tweets promoting newscasts. Public stations offered tweets that promoted programming, other than local newscasts. Ferguson and Greer (2011) also looked at how more than 100 radio stations in
7 the United States used Twitter. The study revealed that music stations get more Twitter followers with promotional tweets, while news stations build their following with tweets that have news updates.
News Industry
The future of print and television news has been in question for years (Ahlers,
2006; Mayer, 2011). Newspaper circulation has dropped off by double digits for some dailies between 2007 and 2008 (Richard, 2008). While newspapers have struggled to maintain reader interest in print platform, researchers have argued that television will also be in crisis (Mayer, 2011; Boyle & Zuegner, 2012). Traditional news media has been faced with striking declines in audiences (Richard, 2008). Blodget (2012) argued that television is not going to disappear and neither will newspapers. However, user behavior will change how the news industry operates and more media will look for ways to reach new audiences (Blodget, 2012). As a result, the entire news industry is in the midst of a digital revolution due to the popularity of the Internet and social media use.
The Internet has caused news organizations to prepare for the future of the industry, from newspaper executives asking whether there will be a print version of their paper in ten years, to television news executives speculating on whether there will be network nightly news (Ahlers, 2006). “Many media analysts and news media executives have claimed that the Internet is a threat to the traditional news media, most notably to network television news and to newspapers” (Ahlers, 2006, p. 30). Online news has created a shift in news consumption. Traditional newspaper readership has dropped more than 50 percent as the number of new communication channels, such as social networks has increased (Sung & Hwang, 2014). Newspapers have responded to the decline by
8 producing more multimedia content for their websites -- a trend known as webvergences
(Thorton & Keith, 2009). Greer and Yan (2011) studied how newspapers in the U.S. responded to changing consumer habits by adopting news digital delivery tools. The researchers found that using a variety of social media sites allowed newspapers to reach readers in new ways. Hermida et al. (2012) found that social networks are becoming a significant source of news for Canadians. Two-fifths of social networking users in the study said they received news from people they follow on social networks. Users added that they valued social media because it helped them keep up with events and exposed them to a wider range of news and information (Hermida et al., 2012). Ahlers (2006) also examined the shift of news consumption from the traditional media to online news media. The study revealed that 22 percent of U.S. adults have substituted some online news for offline news, but a substantial portion of this group viewed online news media acts as a complement rather than as a substitute.
When it comes to news consumption, there is also a growing concern of a media generation gap. Newspapers, news magazines, and television news are losing young consumers and are building business models that do not include them (Ahlers, 2006). It seems as if younger audiences have tuned out. For example, 18 percent of 18 to 29 year old adults watched the nightly network news and only 23 percent of them read a newspaper within the past day (Ahlers, 2006). Recently, Thurman and Newman (2014) investigated news readers’ use of live updating news pages, known as live blogs, and found an average of 15 percent of news consumers use live blogs on a weekly basis. Live blogs have become increasingly popular as an online news format because it is prevalent when covering breaking news and other live events. Blogs can perform well against other
9 news formats in capturing readers’ attention due to the immediacy of posting and sharing information (Thurman & Newman, 2014). Live blogs are just one example of how news organizations have extended their news coverage. “The online versions of most newspapers, television news shows and news networks, and local broadcast TV stations can be seen as product extensions of their existing news products” (Ahlers, 2006, p. 46).
Collins and Brown (2012) also found that television stations are more likely than newspapers to provide mobile content such as text stories for the mobile Web, video for the mobile Web, and weather radar for the mobile Web. The researchers argued that television stations have been producing video for their websites and providing weather radar for awhile and it does not seem to be much of a jump to provide such content in a mobile, on-demand format for news consumers (Collins & Brown, 2012).
Pew Research indicates that the Internet and social media may help achieve some of the core goals of journalism, such as strengthening society, communities, and democracy through sharing information (Gahran, 2011). Therefore, it is important for journalists and news organizations to embrace opportunities brought by social media.
Journalists can fully provide for their audiences through social media by driving traffic to the news site, finding ideas and receiving feedback to stories (Gahran, 2011). In turn, social media allows users to share information provided by journalists. “The more people consume news within social media, the more likely they will share that news with others”
(Weeks & Holbert, 2013, p. 226). Weeks and Holbert (2013) argued that more research is needed to confirm that exposure to news can lead to conversations or online discussions and information sharing.
10 Social media has made its mark in the news industry (Gore, 2014). The success of
Huffington Post and Buzzfeed has demonstrated that online-only news and entertainment publishing can be effective. They have also showed that it is possible to gain a substantial
Web audience without having a print backstory (Gore, 2014). The Atlantic, The
Guardian, and The New York Times among other companies have also been touted for their adaptation to sharing online news and succeeding (Andreessen, 2014). These news organizations have promoted the quality of news while maintaining interaction with its audience (Gore, 2014). “It appears that each medium, television and newspaper, seems to be striking its own path toward what works in convergent media” (Collins & Brown,
2012, p. 259). A review of the literature has shown that as the news industry continues to change, it is ultimately up to the news organizations on whether or not to embrace new platforms. Heo and Park (2013) added when a new media environment is created, traditional media must respond to it and work in a mutually dependent manner, rather than being simply absorbed by the new media environment. Implementation of new technologies and platforms will allow organizations to keep up with the industry and not fall behind. Gore (2014) agreed, “Journalism’s future is bright if it can be many things to many people” (p. 54).
Twitter Use
Twitter is a free microblogging service that was founded in 2006 (Messaging in a
2.0 World). Microblogging is when one posts short thoughts and ideas to a personal blog, particularly by using instant messaging software or a mobile phone (McFedries, 2007).
“Twitter has conquered a leading position as a brand name, rendering the verb
‘twittering’ almost synonymous with microblogging” (Dijck, 2011, p. 344). Twitter
11 combines microblogging with social networking by asking users the question, “What are you doing?” (McFedries, 2007, p. 84). Twitter users respond by posting messages, no more than 140 message characters called tweets (Naaman, Becker, & Gravano, 2011). A tweet is an expression of a moment or idea (Twitter.com, The story of a tweet). The timeline of short messages can range from comments, links and breaking news (Marwick
& Boyd, 2010). In terms of social connectivity, Twitter provides users an opportunity to create a personal profile and connect with others by following other Twitter users (Weeks
& Holbert, 2013). However, self-presentation on Twitter takes place through a user’s tweets and interaction with other users, rather than static profiles. (Marwick & Boyd,
2010).
According to Twitter.com, the social network’s mission is to give everyone the power to create and share ideas and information instantly. The social network suggests for users to tweet meaningful moments, no matter how big or how small. “Quote your grandma, share a photo of your pet sloth, or make a Vine video of your youngest doing a tricycle wheelie. If you think it's interesting, chances are your followers will, too”
(Twitter.com, The story of a tweet). In order to understand how Twitter works, one must understand the structure of Twitter (e.g., username, hashtag). The following terms are used to talk about the structures of Twitter (Twitter.com, The Twitter Glossary). The @ sign is used to identify a user on Twitter. Users mention a @username in tweets to send a message or link. Following is the act of subscribing to a Twitter account. Anyone on
Twitter can follow or unfollow almost anyone else at any time. Users can set their privacy preferences in order for their updates to only be available for their followers
(Naaman et al., 2011). A tweet that is in response to another user's tweet is known as a
12 reply. A tweet that is forwarded and retains original attribution is known as a retweet. In order to favorite a tweet, users tap the star icon to inform the author that his or her tweet was favorited. A hashtag is any word or phrase immediately preceded by the # symbol.
Clicking on a hashtag allows users to see other tweets that contain the same keyword or topic (Twitter.com, The Twitter Glossary).
With 50 million tweets per day, hashtags are central to organizing information on
Twitter. By using hashtags, tweets can be sent to a larger audience rather than a user’s followers (Small, 2011). Ma, Sun, and Cong (2013) proposed methods to predict the popularity of newly emerging hashtags on Twitter. The researchers identified and evaluated the effectiveness of content and contextual features derived from tweets with political candidate hashtags. The study found that contextual features (e.g., tweets containing a mention, tweets containing a retweet) are more effective than content features (e.g., the number of words in a hashtag, digits usage in a hashtag). Hashtags emerged on Twitter in 2007 and have since been used on other social networks such as
Facebook and Instagram (McFedries, 2013). Twitter users have applied hashtags in all sorts of ways. For instance, if a person complains about a relatively unimportant issue and adds the hashtag #FirstWorldProblem, which means a complaint that could be experienced by a privileged person, then other Twitter users will know the person understands how trivial it is (McFedries, 2013).
Twitter’s rapid rise in popularity and adoption is due to a variety of reasons
(Highfield, Harrington, & Bruns, 2013). Twitter is able to connect and support users in a conversation during live events, creating an expression of fandom (Highfield et al.,
2013). Twitter also gives users a live, communal discussion, in which allows their Twitter
13 feeds to update in real-time (Highfield et al., 2013). Researchers suggest Twitter users not only write to the people that are following them on Twitter, but also to themselves
(Marwick & Boyd, 2010). Humphreys, Gill, Krishnamurthy, and Newbury (2013) added that Twitter users express themselves, similar to personal diaries. Naaman et al. (2011) also examined trends on Twitter and found that characteristics of Twitter messages can be categorized based on origin and context, in which can help researchers better understand the trends.
Several researchers (Kwak et al., 2010; Petrović et al., 2013) have looked at the power of information sharing on Twitter. Kwak et al. (2010) examined Twitter and information diffusion, and found that the majority of trending topics are headline news. A closer look at retweets showed that any retweeted tweet reaches an average of 1,000 users no matter what the number of followers of the original tweet. Once retweeted, a tweet gets retweeted again, signifying fast diffusion of information after the first retweet (Kwak et al., 2010). Marwick and Boyd (2010) added that the audience of social media is potentially limitless. They argued for a more specific conception of audience. They suggested that Twitter users have a mental picture of whom they are writing to. “Much like writers, social media participants imagine an audience and tailor their online writing to match” (Marwick & Boyd, 2010, p. 128). Similar to broadcast audiences, social media audiences include random, unknown individuals. However, social media allows personal authenticity and connection among users (Marwick & Boyd, 2010).
Twitter has also been studied as a possible replacement of newswire providers due to the fact that the social network is considered to be a useful source of real-time news.
Petrović et al. (2013) examined the extent of news reporting in newswire and Twitter
14 overlap and whether Twitter often reports news faster than traditional newswire providers. The study found that Twitter reports the same events as newswire providers and many events reported on Twitter are not mentioned in newswire. Although, both streams reported on the same events, there is no evidence that one source leads the other in terms of breaking news (Petrović et al., 2013). As a result, there is little evidence that
Twitter can replace newswire providers. However, the local news that was observed on
Twitter supports the idea that the social network can be used in a localized news setting
(Petrović et al., 2013).
Twitter and News
Social media, most notably Twitter and Facebook, have revolutionized the way news is disseminated and the way news is consumed (Lin & Lazer, 2012). In 2010, Pew
Research found 80 percent of American adults use the Internet and nearly 60 percent of
Internet users use at least one social networking service including, Facebook, Twitter, or
MySpace (Gahran, 2011). The number of adults on social networks suggests that social media is now a mainstream communication and media channel for the core audience for most U.S. organizations (Gahran, 2011). Armstrong and Gao (2011) argued that while news media are in state of transition, the emergence of new technologies makes it an important strategy for newspapers and television stations to connect with their audience through growing online channels. Bruns and Burgess (2012) added that Twitter is the most prominent example of a recent shift in social media. “Twitter is both a social networking site and an information stream” (p. 803). The social networking practices of tweeting have converged with original information sharing content, which is shared on a large scale.
15 Twitter’s uses have been extended beyond everyday “lifesharing” to journalistic activities (Bruns & Burgess, 2012, p. 801). Ahmad (2010) argued that Twitter has become an important addition to the resources of journalists and journalism researchers.
He suggested that by being a useful marketing and research tool, Twitter supports the traditional role of journalists as news information is provided. Researchers (Lasorsa,
Lewis, & Holton, 2012; Xu & Feng, 2014) have agreed that Twitter has changed how journalists engage with their work and found it particularly useful. Lasorsa, Lewis, and
Holton (2012) also revealed that journalists freely express opinion and provide accountability and transparency regarding how they conduct their work on Twitter. Moon and Hadley (2014) studied how journalists use Twitter as a news source and whether
Twitter can lead to changes in traditional newsroom routines by conducting a content analysis of seven mainstream media outlets. They found that journalists embraced Twitter as a new tool for reporting while maintaining specific sources. In addition, the researchers found that television used Twitter more as a sole or primary source than newspapers. Both television and newspapers frequently used Twitter as a source in soft news rather than hard news (Moon & Hadley, 2014).
Newsrooms continue to adjust their strategies and news developing skills
(Armstrong & Gao, 2011). The increasing popularity of social networking sites has traditional news organizations such as The New York Times and The Wall Street Journal intensifying their use of Facebook and Twitter to circulate stories and attract readers
(Schulte, 2009). Twitter has been quickly adopted by news organizations because it is easy and relatively inexpensive to implement in newsrooms (Boyle & Zuegner, 2012).
Twitter-fluent newsrooms and journalists use the tool not only as connection to their
16 websites, but also as a stand-alone channel (Palser, 2009). For example, newsrooms are able to quickly disseminate news through tweets in a breaking news situation. In October
2007, during the Southern California wildfires, news organizations such as the Los
Angeles Times and San Diego public radio KPBS used Twitter to share urgent information including evacuation orders, shelter locations, and firefighting progress
(Armstrong & Gao, 2011). News organizations and reporters have adopted Twitter because of its speed and brevity that is ideal for pushing out breaking news and news updates (Farhi, 2009).
Twitter serves as the platform for first-hand, real-time reports. In 2009, the dramatic Hudson River landing of U.S. Airways Flight 1549 was captured by ferry passenger, Janis Krums, who took a photo with a cell phone and sent it out via Twitter
(Hermida, 2010; Murthy, 2011). The image spread so quickly; heavy online traffic caused Twitter to crash. The photograph received nearly 4,000 views within four hours of posting to Twitter (Watzko, 2012). According to Lin and Lazer (2012), before CNN confirmed that U.S. Navy SEALS had killed Osama bin Laden on May 2, 2011, millions of social media users had rapidly dispersed the information through their Twitter and
Facebook pages. Mainstream media learned of the news before President Obama’s official press conference from a tweet by Keith Urbahn, a staff member of former
Defense Secretary Donald Rumsfield (Moon & Hadley, 2014). Urbahn’s tweet was retweeted by his followers, resulting in the rapid spread of the news (Moon & Hadley,
2014). The instant publishing capabilities of social media sites has allowed individuals as well as news outlets to watch the events unfold in real time around the globe (Lin &
Lazer, 2012).
17 More newspapers are turning to Twitter to reach an online audience. In 2012,
Boyle and Zuegner examined how 70 U.S. mid-sized newspapers used Twitter to disseminate news and information, and found newspapers tweeted an average of 82 times a week, sharing mostly local content. The study also found there was no relationship between a newspaper’s size and the number of tweets composed. In other words, a newspaper’s size does not mean that the newspaper will have more or fewer tweets. The study showed that newspapers with more followers tweeted more often compared to newspapers with fewer followers (Boyle & Zuegner, 2012). However, the researchers argued the newspapers did not use Twitter in the most effective manner. For example, some newspapers used automated Twitter feeds, which tweeted the same headline used in the print edition. The researchers suggest that newspapers be more social and encourage interaction with readers (Boyle & Zuegner, 2012). Palser (2009) also added that automatically feeding Web headlines to Twitter streams is an acceptable start, but not the best use of the format. Wotzko (2012) proposed a model to combine Boal’s Newspaper
Theatre with Twitter as an educational exercise in reconsidering and reframing the messages broadcast in the mainstream media. Newspaper Theatre is used to teach people how to reread the news and headlines to make their own theatre. He suggested that the methods of Newspaper Theatre could be applied to the communication conventions of
Twitter, making such methods as Newspaper Twitter. The real time nature of Twitter allows Newspaper Twitter to take place as stories emerge, perhaps before a print or online newspaper can publish an extended narrative (Wotzko, 2012).
Farhi (2009) argued that Twitter could also be used as a community-organizing tool for the newsroom. When the Iowa Supreme Court ruled in favor of same-sex
18 marriages in the state, the Des Moines Register created the #iagaymarriage hashtag. The hashtag became so popular that competitors, such as The Gazette in Cedar Rapids began tagging their tweets with it (Farhi, 2009). There was a similar situation in Everett,
Washington when the area was flooded. The Herald’s #waflood hashtag became the go- to code for bulletins affecting the community (Farhi, 2009).
Twitter also plays an important role in fostering communication around live televisions events such as political debates, sporting events, primetime broadcasts, and other local scale gatherings (Lin, Keegan, Margolin, Lazer, 2014). Television producers increasingly encourage viewers to use a second screen in order to share their reactions to a program’s content via Twitter (Lin et al., 2014). The study examined how collective patterns of user behaviors under conditions of shared attention are distinct from other activity such as breaking news events. Researchers found that media events not only generate large volumes of tweets, but they are also associated with declines of interpersonal communication, more highly concentrated attention by replaying and retweeting users, and elite users predominately benefiting from this attention (Lin et al.,
2014). With the evolution of traditional news media increasing attention to social media to provide news content, it is important to develop a deeper understanding of how social media users are interacting with news organizations (Boyle & Zuegner, 2012;
Rosenberry, 2013).
News is becoming a participatory activity as social media users contribute their own stories and experiences and post their reactions to events (Purcell et al., 2010). Pew
Research found that 37 percent of Internet users, described as news participants, actively contributed to the creation, commentary, or dissemination of news (Purcell et al., 2010).
19 It has been argued that the most important function of this new participatory media is that it acts as a bridge between traditional media, both online and offline (Veenstra et al.,
2013).
Murthy (2011) examined participatory journalism or citizen journalism on
Twitter. The study explored whether Twitter has transformed ordinary individuals as citizen journalists whom the news reading public follows or their voices are included by traditional media and found that professional news outlets have become more open to using tweets for picking up breaking news. However, the public ultimately takes interest in the stories themselves and not so much in the original source tweets or the individual
Twitter user responsible for breaking the story (Murthy, 2011). Franklin (2014) agreed that Twitter plays an important role in participatory forms of citizen journalism, even if the format requires journalistic skills and tabloid compression to be highly developed.
Knight (2012) studied the coverage of the 2009 Iranian elections. Young protestors were using Twitter, Facebook, and YouTube to tell the world what was going on in their streets. News organizations were able to take advantage of the social media tools in their coverage (Knight, 2012). Knight (2012) argued that although the Internet is a place where all voices are equal and have equal access to the public discourse, the practices of journalists are heard above the crowd (Knight, 2012).
Based on a review of literature, is it clear that the goal of tweeting is to enhance one’s cyberspace presence in social media (McFedries, 2007). As the main focus of
Twitter, information sharing is a powerful tool for communicating with other users.
Social media presence is an important aspect of news coverage by mainstream news organizations. It not only helps drive online traffic, but it also allows a news organization
20 to interact with its audience. Audience engagement with social media helps journalists identify and connect with people who are the most interested in news content
(Rosenberry, 2013).
Gibbed and Bernas (2009) noted that television news and newspapers are dominant outlets for news content and it is important to study how those outlets adapt to new media trends. Although, researchers have studied local news media’s Twitter activity, there is a gap of research comparing how local television news and local newspapers use Twitter. There is also little research comparing strategies such as tweet structure and content that television news stations and newspapers use on Twitter. Thus, this study seeks to address the gap by conducting a content analysis of tweets composed by local television news stations and local newspapers. The study examined tweet structure and content in order to learn more about news organizations’ Twitter strategies.
In addition, the Twitter activity of local news media was analyzed for audience engagement and promotion information.
Newsrooms need to know what is happening on Twitter and consider the opportunities it offers (Palser, 2009). This study focuses on local television news stations and local newspapers because of the differences between the two traditional platforms.
Local television provides visual news content, while local newspapers offer deeper, investigative storytelling through print. Twitter can lead to changes in traditional newsroom routines (Moon & Hadley, 2014). It is not known if Twitter has changed how local news media provide news content. Thus, this study seeks to compare the local television stations and newspapers’ Twitter activity.
21 The following research questions are proposed.
RQ1: What Twitter strategies do local television news stations use (e.g., news
content, news topic, promotion activity)?
RQ2: What Twitter strategies do local newspapers use (e.g., news content, news
topic, promotion activity)?
RQ3: What are the differences between local television news stations and local
newspapers’ Twitter use?
RQ4: What are the relationships between and among audience engagement, news
content, Twitter structure, and promotion activity?
22 CHAPTER III
METHODOLOGY
This study examined the similarities and differences in Twitter use between local television news stations and local newspapers across the United States. Several scholars suggest that content analysis can help compare news trends among local media outlets
(Greer & Ferguson, 2011; Lasorsa, Lewis, & Holton, 2012). Data collection took place within the most important time in local television news known as “sweeps.” During this time, the Nielsen Company measures local television viewing in homes across 210 television markets. “Sweeps” takes place in February, May, July, and November
(Nielsen, 2014).
Sample and Unit of Analysis
According to Nielsen (2013), there are 210 designated media markets, in which television viewing is measured (see APPENDIX A). In this study, 30 media markets
(e.g., 10 large, 10 medium, 10 small) were randomly selected, in order to establish a variety of local news organizations. The media directory, USNPL.com was used as a reference to randomly choose one local television news station and one local newspaper from each selected media market for a total of 30 local newspapers and 30 local television news stations. As a result, this study examined tweets for a total of 60 news organizations (see APPENDIX B) that were composed during November 1, 2014 and
23 November 30, 2014. Twitter.com was the source of a local news station and local newspaper’s tweets. Only tweets composed by a news station or newspaper’s official
Twitter username were analyzed in this study. A composition week of November was used in this study. Days were assigned for each week of the month (e.g., local television news stations and newspapers were measured on Monday in the first week of November,
Tuesday in the second week of November, etc.). All of the tweets on the selected days were coded. Thus, 2,390 tweets composed by 30 local television news stations and 2,117 tweets composed by 30 local newspapers were coded in this study; a total of 4,507 tweets from 60 news organizations were examined to study the strategies of local news media’s
Twitter use.
Coding
Tweets of each local media were assessed and analyzed. Overall, eleven variables were coded in this study, including: type of news organization, media market size, day of the week, total number of tweets, total number of followers, time of tweets, tweet structures, tweets with audience engagement, tweet news content, tweet topics, and tweet promotion activity (see APPENDIX C).
Type of news organization was coded as television news station or newspaper.
Media market size was coded as large, medium, or small. Markets 1 through 50 were considered large, markets 51 through 100 were considered medium, and markets above
100 were considered small. The total number of tweets and total number of followers were coded based on how many tweets are listed on the new organization’s Twitter homepage. In addition, tweets were coded based on when they are composed. The day of week was coded based on what day of the week it was composed (e.g., Monday,
24 Tuesday, etc.). Time of tweet was coded based on which hour the tweet was composed within (e.g., 1 a.m., 2 a.m., etc.). Tweets structure was coded based on the element of each tweet, including username, hashtag, photo, video, and link attached. Audience engagement was coded as the number of retweets and the number of favorites of each tweet.
Tweet news content was coded based on whether tweets include news categories of breaking, business, crime, education, entertainment, health, human interest, politics/government, sports, traffic, and weather. If a tweet did not appear to fit in the news content categories, it was coded as other. Tweet topics were coded based on the goal of the tweet, including calls for actions, expresses emotion, interacts with audience, and shares information. Calls for action was coded if a tweet tells Twitter users to do something, such as retweet or tune in. Expresses emotion was coded if a tweet describes the mood or attitude of the news organization. Interacts with audience was coded if a tweet is connecting or communicating with other Twitter users. Sharing information was coded if a tweet provides general news information. A tweet may have more than one topic, coders were asked to code all topics that apply. The promotion activity of each tweet was based on the promotion module of three promotional activities (Greer &
Ferguson, 2011). The tweets were coded in terms of general organization promotion, strategic promotion, and targeted audience promotion. General organization promotion was coded when the tweet generally promotes the news organization. Strategic promotion was coded when the tweet promotes contests or contests winners, promotion for organizations or events outside of the news organization. Targeted audience promotion
25 was coded when the tweet reaches out to the audience by promoting on-air or online programming.
The researcher and two other graduate students carried out the coding independently. Coders were trained using a preliminary subset of tweets. The training process continued until all three coders were comfortable with the categories that are provided for coding. Definitions and examples of the various categories were provided for coding (see APPENDIX D). All selected tweets were downloaded to a computer hard drive for the purpose of coding and intercoder reliability testing.
To test intercoder reliability, the three coders coded 20% of the sample that were randomly selected from coding materials, including at least one local television news station and local newspaper. An intercoder reliability check used Cohen’s Kappa to run on the variables (e.g., audience engagement, tweet news content, tweet topic, and tweet promotional activity) that required a judgment call from the coders. The measure of agreement was .92 for audience engagement, .85 for tweet news content, .79 for tweet topic, and .87 for tweet promotion activity, which indicates an overall reliability of .86, a high level of reliability on the coding instrument and among the coders.
26 CHAPTER IV
RESULTS
A total of 4,507 tweets were assessed from 60 local news organizations. There were 2,390 (53%) tweets composed by local television stations and 2,117 (47%) tweets composed by local newspapers.
When looking at the news industry as a whole, large media markets tweeted more frequently with 1,806 (40%) tweets compared to 1,525 (33.8%) in medium markets and
1,176 (26.1%) in small markets. The number of tweets composed on each day of the week varied. The top two days most frequently tweeted was Tuesday with 803 (17.8%) tweets and Friday with 790 (17.5%) tweets, followed by Thursday with 733 (16.3%) tweets. The three most frequent times to tweet were 6 p.m. with 324 (7.2%), 3 p.m. with
320 (7.1%) tweets, and 4 p.m. with 320 (7.1%) tweets.
This study found links to be the most frequently used structure with 4,101 (91%) tweets using links. The second most frequently used structure were photos or videos with
1,242 (27.5%) tweets. In addition, there were 1,192 (26.4%) tweets with hashtags and
795 (17.6%) tweets with usernames. In terms of audience engagement, there were 2,157
(47.9%) tweets that had been retweeted and 1,803 (40%) tweets that were favorited.
In terms of tweet content, crime news was tweeted most often with 1,374 (30%) tweets followed by political 608 (13.5%), human interest 595 (13.2%), business 553
27 (12.3%), sports 496 (11%), weather 424 (9.4%), other 392 (8.7%), education 330 (7.3%), traffic 240 (5.3%), health 205 (4.5%), entertainment 191 (4.2%), and breaking news 98
(2.2%) tweets. The most frequently tweeted topic was sharing information with 4,341
(96.3) tweets, followed by interacting with audience with 422 (9.4) tweets, calling for action with 303 (6.7%) tweets, and expressing emotion with 77 (1.7%) tweets. Although, tweets varied in news content and tweet topic, promotion activity was not very frequent.
There were 242 (5.4%) tweets that had targeted audience promotions, while 174 (3.9%) tweets had general organization promotions and 64 (1.4%) tweets had strategic promotions.
In response to RQ1, local television stations had a mean of 29,224 total tweets and 28,040 Twitter followers. Twitter activity varied for days of the week. The top two most frequently tweeted days of the week was Friday with 420 (17.6%) tweets and
Tuesday with 414 (17.3%) tweets. Television stations tweeted less frequently on the weekend, Sunday had 240 (10%) tweets and Saturday had 177 (7.4%) tweets. The most frequently tweeted hours were 6 p.m. with 194 (8.1%) tweets and 5 p.m. with 175 (7.3%) tweets.
In terms of tweet structure, results showed links were the most frequently used with 2,049 (85%) tweets for local television stations. The second most frequently used structure was hashtags with 818 (34.2%) tweets. There were 401 (16.8%) tweets with usernames and 622 (26%) tweets with photos or videos attached. When comparing news content, the most frequently tweeted category was crime news with 853 (35.7%) tweets followed by weather 318 (13.3%), human interest 262 (11%), political 256 (10.7%), sports 233 (9.7%), other 227 (9.5%), traffic 165 (6.9%), education 162 (6.8%), business
28 161 (6.7%), health 116 (4.9%), breaking 80 (3.3%), and entertainment 65 (2.7%). The most frequently used topic was sharing information with 2,303 (96.4%) tweets, followed by calling for action with 184 (7.7%) tweets, interacting with audience 176 (7.4%) tweets, and expressing emotion with 10 (.4%) tweets. In addition, the most frequent promotion activity used was targeted audience promotions with 201 (8.4%) tweets. There were 122 (5.1%) tweets with general organization promotions and 36 (1.5%) tweets with strategic promotions.
In response to RQ2, local newspapers had a mean of 28,213 tweets and 20,237
Twitter followers. Local newspapers’ Twitter activity appeared to be consistent throughout the weekdays. The most frequently tweeted days of the week were Tuesday with 389 (18.4%) tweets and Friday with 370 (17.5%) tweets. Tweets were much less frequent on the weekend, Saturday had 209 (9.9%) tweets and Sunday had 154 (7.3%) tweets. The most frequently tweeted hours were 9 a.m. with 172 (8.1%) tweets and noon with 172 (8.1%) tweets.
The most frequently used tweet structure was links with 2,052 (96.9%) tweets.
The second frequently used structure was photos or videos with 620 (29.3%) tweets.
There were 394 (18.6%) tweets with usernames and 374 (17.7%) tweets with hashtags.
When comparing news content, the most frequently tweeted category was crime news with 521 (24.6%) tweets followed by business 392 (18.5), political 352 (16.6%), human
Interest 333 (15.7%), sports 263 (12.4%), education 168 (7.9%), other 165 (7.8%), entertainment 126 (6%), weather 106 (5%), health 89 (4.2%), traffic 75 (3.5%), and breaking 18 (.9%). In terms of tweet topic, the most frequently used was sharing information with 2,038 (96.3%) tweets, followed by interacting with audience 246
29 (11.6%), calling for action 119 (5.6%) and expressing emotion 67 (3.2%). Additionally, there were more frequently used tweets with general organization promotions 52 (2.5%) compared to targeted audience promotions 41 (1.9%) and strategic promotions 28 (1.3%).
To answer RQ3, chi-square and independent t-tests examined the differences and similarities of local televisions news stations and local newspapers’ Twitter use. Results found significant differences between local television stations and local newspapers and market size (X2=137.396; p<.0001). Large markets produced more tweets in both media platforms. However, local television news stations tweeted more frequently in medium markets with 954 (39.9%) tweets compared to local newspapers (571, 27%), but tweeted significantly less often compared to newspapers in small markets (707, 33.4%). Results also found significant differences between local television stations and local newspapers and day of the week (X2=20.954; p=.002). Local television stations tweeted more frequently on Fridays with 420 (17.6%) compared to local newspapers 370 (17.5%).
However, local newspapers tweeted more frequently on Tuesdays (389, 18.4%) compared to local television stations (414, 17.3%). Both platforms tweeted significantly less on the weekends. Local newspapers tweeted more frequently on Saturday 209 (9.9%) compared to local television stations (177, 7.4%). However, local television stations tweeted more frequently on Sundays (240, 10%) compared to local newspapers (154,
7.3%).
Significant differences were found between local television news stations and local newspapers and tweets with a hashtag (X2=160.054; p<.0001), photo or video
(X2=9.181; p=.027) , and link attached (X2=170.147; p<.0001). Local television stations used significantly more hashtags and photos or videos than local newspapers, while local
30 newspapers used links and photos significantly more frequently than local television stations. There were no significant differences in tweets with usernames (X2=6.921; p=.074). In terms of news content, significant differences were found between local television news stations and local newspapers and tweets regarding breaking (X2=34.919; p<.0001), business (X2=144.731; p<.0001), crime (X2=65.035; p<.0001), entertainment
(X2=28.899; p<.001), human interest (X2=22.267; p<.0001), political (X2=33.667; p<.0001), sports (X2=9.056; p=.011), traffic (X2=25.154; p<.0001), weather (X2=90.794; p<.0001) and other (X2=4.104; p=.043) news. Local television stations tweeted news about crime, weather, traffic, breaking news and other news significantly more often than local newspapers. On the other hand, local newspapers tweeted human interest, sports, political, health, and entertainment news more frequently than local television stations.
There were no significant differences with tweets regarding education (X2=2.217; p=.137) or health (X2=1.097; p=.295) news.
In addition, as seen in Table 1, there were significant differences found between local television news stations and local newspapers and tweet topics that called for action
(X2=7.724; p=.005), expressed emotion (X2=50.399; p<.0001), and interacted with audience (X2=23.963; p<.0001). Local newspapers composed tweets that interacted with their audience or expressed emotion significantly more often than local television stations. However, local television stations composed tweets that called for action more frequently than local newspapers. There was no significant difference in tweets that shared information (X2=.027; p=.871). The study also analyzed promotion activity among
Twitter use of local television news stations and local newspapers. Significant differences were found among tweets of general organization promotions (X2=22.313; p<.0001) and
31 targeted audience promotions (X2=92.582; p<.0001). Local newspapers tweeted general promotions more frequently compared to local televisions stations. On the other hand, local television stations tweeted targeted audience promotions significantly more than local newspapers. There was no significant difference in strategic promotion (X2=.270; p=.603).
Table 1. Differences in Twitter Strategy between Local TV Stations and Newspapers
Local TV Local Stations Newspapers Twitter Strategies Freq. % Freq. % X2 Structures Links 2,049 85.0% 2,052 96.9% 170.147** Hashtags 818 34.2% 374 17.7% 160.054** Photos/Videos 622 26.0% 620 29.3% 9.181* Usernames 401 16.8% 394 18.6% 6.921
News Content Crime 853 35.7% 521 24.6% 65.035** Weather 318 13.3% 106 5.0% 90.794** Human Interest 262 11.0% 333 15.7% 22.267** Political 256 10.7% 352 16.6% 33.667** Sports 233 9.7% 263 12.4% 9.056* Other 227 9.5% 165 7.8% 4.104* Traffic 165 6.9% 75 3.5% 25.154** Education 162 6.8% 168 7.9% 2.217 Business 161 6.7% 392 18.5% 144.731** Health 116 4.9% 89 4.2% 1.097 Breaking 80 3.3% 18 .9% 34.919** Entertainment 65 2.7% 126 6.0% 28.899**
News Topic Sharing Information 2,303 94.4% 2,038 96.3% .027 Call for Action 184 7.7% 119 5.6% 7.724** Interact with Audience 176 7.4% 246 11.6% 23.963** Express Emotion 10 .4% 67 3.2% 50.399**
Promotion Targeted Audience 201 8.4% 41 1.9% 92.582** General Organization 122 5.1% 52 2.5% 22.313** Strategic 36 1.5% 28 1.3% .270 **p<.01; *p>.05
32 An independent t-test suggests significant differences between local television news stations and local newspapers the number of tweets (t=2.295; p=.023) and the number of Twitter followers (t=13.447; p<.0001), as shown in Table 2. In addition, an independent t-test suggests that there was a significant difference between local television news stations and local newspapers on the likelihood of a tweet to be retweeted (t=3.648; p<.0001). There was no significant difference between local television news stations and local newspapers on the likelihood of a tweet to be favorited (t=1.722; p=.085).
Table 2. Differences in Twitter Use between Local TV Stations and Newspapers
Local TV Stations Local Newspapers Variables M SD M SD t Number of Tweets 29,224.31 13739.310 28,213.80 15813.741 2.295* Number of Followers 28,040.04 21606.640 20,237.16 16664.558 13.447* Number of Retweets 2.01 6.645 1.29 6.493 3.648* Number of Favorites 1.58 5.169 1.18 10.034 1.722 *p>.05; N = 4,507 (2,390 Local Television News Stations; 2,117 Local Newspapers)
In response to RQ4, correlation tests examined the relationships between audience engagement (e.g. retweets and favorites), tweet structure (e.g. username, hashtag, etc.), tweet news content (e.g., breaking, business, etc.), and promotion activity (general organization promotion, strategic promotion, etc.). There was a significant, positive correlation between numbers of retweets and numbers of favorites (r=.784, p<.0001). The results indicate the more retweets a tweet received, the more favorites it also received.
There were positive correlations between number of retweets and tweets regarding breaking (r=.060, p<.0001) and sports (r=.048, p<.0001) news content. The results suggest tweets regarding breaking news or sports stories, received more retweets. There were positive correlations between number of favorites and tweets regarding
33 entertainment (r=.086, p<.0001) and sports (r=.40, p=.007) news content. The results suggest tweets regarding entertainment or sports news, the more favorites it will receive.
There were positive correlations between tweets with usernames and the number of retweets (r=.052, p<.0001) and favorites (r=.056, p<.0001). This suggests that when a tweet mentions a username, the more retweets and favorites that tweet will receive. There was a positive correlation between tweets with hashtags and the number of retweets
(r=.083, p<.0001) and favorites (r=.045, p=.003). The results suggest that when a tweet has a hashtag, the more retweets and favorites it will receive. There were positive correlations between tweets with photos or videos and the number of retweets (r=.107, p<.0001) and favorites (r=.093, p<.0001). There was a negative correlation between tweets with links attached and the number of retweets (r=-.043, p=.004).
There was a positive correlation between tweets with usernames and targeted audience promotions (r=.060, p<.0001). The results suggest that the more tweets with usernames are composed, the more promotion activity targeted toward the organization’s audience. There were positive correlations between tweets with hashtags and general organization promotions (r=.078, p=.0001) and targeted audience promotions (r=.047, p=.001). There were also negative correlations between tweets with links and general organization promotions (r=-.041, p=.006), strategic promotions (r=-.061, p<.0001), and targeted audience promotions (r=-.184, p<.0001). The results suggest the more promotion activity; the less links are present in tweets.
34 CHAPTER V
DISCUSSION
This study examined how local television news stations and local newspapers in the United States use Twitter. Specifically, this study focused on tweet structure, news content, tweet topic, audience engagement, and promotion activity and compared the differences between televisions stations and newspapers.
Results reveal key concepts that lead to stronger audience engagement with a news organization’s Twitter followers. In order for a tweet to receive more retweets and favorites, findings suggest to incorporate tweet structures (e.g. usernames, hashtags, photos, etc.) when composing a tweet. The tweets with more tweet structures will make a stronger impression on Twitter followers of a news organization. If a news organization is looking to compose a tweet to generate traffic with a link attached, the tweet should also incorporate some other tweet structure such as usernames, hashtags and photo or videos. In addition to audience engagement, retweets have a strong relationship to favorites. The more interaction through tweet structures will lead to more retweets and favorites, a higher level of audience engagement. This may be an interesting strategy for news organizations to utilize in order to grab the attention of followers.
Although, local news media varied in content, tweets regarding breaking news received more retweets. News organizations should attempt to follow breaking news and
35 provide updates via Twitter; however, news organizations should incorporate Twitter structures within breaking news tweets such as #BREAKING or #JustIn. These suggestions are based on the findings that tweet structures help grab the attention of followers and lead to more retweets, in which will lead to higher audience engagement.
By utilizing Twitter structures, news organizations can build consistent promotion activity. For instance, local news organizations could incorporate a hashtag or photo of their logo to promote on-air or online programming. Greer and Ferguson (2011) suggested for stations to include news stories in their tweets to better connect with potential followers. The different structures (hashtags, photos, etc.) allow news organizations to share information, but enhance their loyalty to the news stations or newspaper (Greer & Ferguson, 2011).
When looking at the news industry as a whole, television news stations tweet more than local newspapers. In general, large media markets tweeted more. Weekdays proved to be a prime time to tweet, versus the weekend that had low Twitter activity. The middle of the day was also a popular time to tweet, with spikes of activity early in the morning and evening. Overall, crime news was the most significantly tweeted news content. Interestingly, local televisions stations also tweeted weather news compared to local newspapers tweeting business news. This explains that local news media is providing their traditional news content via Twitter. However, Twitter poses as another avenue for local news organizations to disseminate content more frequently than their traditional forms. These findings are important to guide newsroom managers on what kind of content should be distributed via Twitter. For instance, local newspapers may want to tweet more often regarding weather and traffic news throughout the day, rather
36 than in the mornings only. This study also suggests for news organizations to continue to tweet frequently and incorporate tweet structures in order to build stronger audience engagement. Hashtags were the least used tweet structure, overall. This is an interesting finding because of the increasing popularity of hashtag use in media trends. Hashtags allow organizations to target a specific audience and by using a localized hashtag, users may feel a sense of interaction (Lachlan et al., 2014). Hashtags allow users to become part of an online community conversation, in which local news organizations should generate new conversations by using hashtags when tweeting. In addition, by using hashtags, tweets can be sent to a larger audience than a news organization’s followers
(Small, 2011). As a result, the more hashtags used, the greater the impression a tweet will have on the audience.
In addition, the most tweeted topic was sharing information among local television stations and local newspapers. This finding is not surprising because of the purpose of the social network. Twitter is a service that allows users to discover and receive content from sources that are of interest, as well as share the content with other users (Twitter.com). As a result, Twitter allows local news media to provide interesting content to their audience. However, Twitter does not appear to be a popular platform for promotion activity. Newsroom managers should take advantage of Twitter’s full potential and promote the organization, content, contests, and programming to the organization’s website. The findings of this study also suggest incorporating promotions within a tweet, but utilizing usernames, hashtags, and photos or videos. For example, including a hashtag or picture of the organization can be a simple way to promote the organization and generate interactivity at the same time. Interactive features suggest that interactivity gains
37 more followers (Greer & Ferguson, 2011). Twitter’s microblogging format and limit of
140 characters is ideal for local news media to tweet news stories with tweet structures, such as hashtags and links to generate more traffic to their website (Greer & Yan, 2011).
Furthermore, local television stations and local newspapers use Twitter differently. As expected, large markets for both media platforms tweeted more. Local television stations tweeted more than local newspapers in medium markets. However, local newspapers tweeted more frequently than local television stations in small markets.
The findings suggest that smaller newspapers strive to find more ways to use Twitter to reach out to its unique audience (Boyle, 2012). Greer and Ferguson (2011) also noted that local news organizations aim to connect with their community. Local newspapers in small communities may realize the potential Twitter has as a cost-effective way to connect with their audience at any time. As newspapers struggle to stay alive in today’s news industry, Twitter allows local newspapers to disseminate content and potentially, reach a bigger audience. The frequent Twitter activity in small communities may also show the effort local newspapers are doing to keep up larger news markets in producing content. The findings reaffirm that a newspaper’s size does not necessarily mean the newspaper will tweet more often (Boyle & Zuegner, 2012).
Additional differences include the specific times each platform would commonly tweet. Local television news stations tweeted more during the evening hours of 6 p.m. and 5 p.m. Local newspapers tweeted more during the morning hours of 9 a.m. and 12 p.m. The time differences demonstrate the pace of daily newsroom operations for both media platforms. Local television stations are typically busy preparing for the evening news. Once stories are produced and posted to the station website, they are tweeted out.
38 On the other hand, local newspapers publish the night before distribution and tweet first thing in the morning. The findings may suggest that local television stations are tweeting their top stories of the day or promoting their evening news programs and local newspapers are tweeting their top headlines early in the day. Local news media may want to build consistent Twitter activity by tweeting frequently throughout the day. News organizations may consider having specific employees to manage Twitter activity and designate time to tweet specific content such as news stories and promotions.
In terms of tweet structures, local televisions stations and local newspapers utilized usernames, hashtags, photos or videos, and links when tweeting. However, a main difference between the media platforms and tweet structure is that local television stations used hashtags more than local newspapers. Newspapers have not fully adopted the concept of hashtags and are not using tweet structures to their full potential (Greer &
Yan, 2011). The findings suggest that it is important to use hashtags when tweeting because it can push traffic to websites of local news organizations (Small, 2011). As a result, the scope of a news story can go beyond the audience when incorporating hashtags, photos or videos, rather than only using links.
Both media platforms shared some similarities regarding news content. Local television news stations and local newspapers tweeted more crime stories than any other news content category. The findings suggest that crime is popular content to tweet because it is deemed newsworthy. Crime news includes stories related to violence and scandal, in which, news organizations may put more precedence when considering content to tweet out. Breaking news stories were the least tweeted news content for both local television news stations and local newspapers. This finding emphasizes the rarity of
39 breaking news on a regular basis. Breaking news does not happen as often, in turn, is not tweeted as commonly. News organizations may not want to over tweet stories, terming them as breaking news because it if over used, stories loses its legitimacy. News organizations also do not want to lose credibility of what they consider breaking news.
In addition, local television news stations and local newspapers both shared information as their main tweet topic. This finding emphasizes that sharing information is the true purpose of news organizations. Local televisions news stations and local newspapers’ primary focus is to share news and keep their audience informed. Twitter appears to be another vehicle for news organizations to disseminate content. News organizations typically forward print and broadcast stories to their news feeds, delivering the same news over a different platform (Armstrong & Gao, 2010). The findings reaffirm research that Twitter has proven to be a valuable tool in sharing information (Greer &
Ferguson, 2011; Hagman, 2012; Rosenberry, 2013).
Another similarity between local television news stations and local newspapers is promotion activity. Overall, Twitter did not appear to be a popular outlet for news organizations to produce regular promotion activity. However, when promotion activity was present, the media platforms utilized Twitter differently. Local television stations focused on targeted audience promotion more. The findings suggest television stations promoted on-air or online programming via Twitter. Local newspapers emphasized generalize organization promotions. The findings suggest that newspapers tweeted more to reinforce their brand and image to their Twitter followers.
40 Limitations and Future Studies
A few limitations should be noted regarding this research. First, this study presents a glimpse of Twitter feeds of local television news stations and local newspapers. This study looked at a total of 60 news organizations; 30 media markets only. The sample was selected based on television media markets. Future research may consider looking at the unique structures of news distribution regarding circulation and population. This study also only examined tweets that were composed by the official
Twitter account of each news organization. It did not include tweets from journalists of local television news stations and local newspapers. Future studies may look to see if the
Twitter strategies of anchors and reporters are similar to news organizations.
It is also important to note the daily news events during the time of data collection because news organizations produce tweets based on current events. During November of
2014, riots continued across the country in protest of the Missouri Grand Jury deciding not to indict Officer Darren Wilson for the shooting of teenager Michael Brown. Also, during this time, Minnesota Viking running back Adrian Peterson pleads no contest to the charge of assault of a child. Same-sex marriage and immigration were also popular news topics during the time. Another limitation is that content analysis simply described the common practice of Twitter use of local news media. The method does not explain why local news organizations tweet the way they do. It is suggested to consult with news managers to discuss the findings for further interpretation.
Despite the limitations, this study offers valuable insight for local news media to strategize their Twitter use. First, local organizations should fully embrace Twitter and its capabilities, in order to stay connected with followers, as well as reach new audiences.
41 Local television stations and local newspapers should tweet frequently and incorporate tweet structures (e.g. links, hashtags, photos, videos) in order to build stronger audience engagement. News managers may want to consider extending an organization’s Twitter content to more than traditional news reporting. For instance, local news outlets could feature their anchors or reporters, as well as include activity of behind-the-scenes, in order to interact more with their audience. In addition, tweeting promotion information, such as upcoming events, programming, or contests will build a stronger online presence for a local news organization.
Future research may examine Twitter followers of local news media to determine their motivations for following television new stations or newspapers and how they generally make use of the new organizations’ tweets. This study analyzed only retweets and favorites of each tweet; future studies may examine how many replies a tweet may have. Additional research is also needed to see if news organizations have specific employees to provide social media content. It would be beneficial to understand how employees decide on Twitter content.
This study documented how local news organizations explore the uses of Twitter regarding news content, audience engagement, and promotion. By comparing tweets of local televisions stations and local newspapers, the study provided a better indication of how local news media uses Twitter and engages with online audiences. Newsroom managers and journalists can adopt specific strategies to integrate into their daily Twitter activity, in order to interact and connect with audiences. If local news media utilized
Twitter to the maximum effectiveness, organizations would create a strong social media presence. This research also extends the literature by applying cross-media promotion to
42 Twitter to understand how news organizations are adapting to an ever-changing media environment (Greer & Ferguson, 2011). As the news industry continues to go digital, news organizations need to be accepting of new media platforms to attract and maintain audiences.
43 REFERENCES
Ahlers, D. (2006). News consumption and the new electronic media. Harvard International Journal Of Press/Politics, 11(1), 29-52. doi:10.1177/1081180X05284317
Ahmad, A. (2010). Is Twitter a useful tool for journalists?. Journal Of Media Practice, 11(2), 145-155. doi:10.1386/jmpr.11.2.145_1
Andreessen, M. (2014 Feb 25). The future of the news business. Andreessen Horowitz: Software is Eating the World. Retrieved from http://a16z.com/2014/02/25/future- of-news-business/
Arceneaux, N., & Weiss, A. (2010). Seems stupid until you try it: press coverage of Twitter. New Media & Society, 12(8), 1262-1279. doi:10.1177/1461444809360773
Armstrong, C. L., & Gao, F. (2011). Gender, Twitter, and news content. Journalism Studies, 12(4), 490-505. doi:10.1080/1461670X.2010.527548
Ausserhofer, J., & Maireder, A. (2013). National politics on Twitter. Information, Communication & Society, 16(3), 291-314. doi:10.1080/1369118X.2012.756050
Blodget, H. (2012 June 3). Don’t mean to be alarmist, but the TV business may be starting to collapse. Business Insider. Retrieved from http://www.businessinsider.com/tv-business-collapse-2012-6
Bødker, S., & Petersen, A. B. (2007). Seeds of cross-media production. Computer Supported Cooperative Work: The Journal Of Collaborative Computing, 16(6), 539-566. doi:10.1007/s10606-007-9056-1
Boyle, K. (2012). Latter-day tweets: The Mormon times 's use of Twitter as a reporting tool. Journal Of Media & Religion, 11(4), 189-199. doi:10.1080/15348423.2012.730320
Boyle, K., & Zuegner, C. (2012). News staffs use Twitter to interact with readers. Newspaper Research Journal, 33(4), 6-19. Retrieved from http://content.ebscohost.com.lib.ezproxy.uakron.edu:2048/ContentServer.asp?T= P&P=AN&K=84111120&S=R&D=ufh&EbscoContent=dGJyMMvl7ESeprE4zO X0OLCmr0yeqK5Ssq24TbSWxWXS&ContentCustomer=dGJyMPGvrkixr7VNu ePfgeyx44Dt6fIA
44 Bruns, A., & Burgess, J. (2012). Researching news discussion on Twitter. Journalism Studies, 13(5/6), 801-814. doi:10.1080/1461670X.2012.664428
Cha, M., Benevenuto, F., Haddadi, H., & Gummadi, K. (2012). The world of connections and information flow in Twitter. IEEE Transactions On Systems, Man & Cybernetics: Part A, 42(4), 991-998. doi:10.1109/TSMCA.2012.2183359
Chyi, H. I. (2009). Information surplus in the digital age: Impact and implications. In Z. Papacharissi (Ed.), Journalism and citizenship: New agendas (pp. 91-107). New York: Lawrence Erlbaum/Taylor and Francis.
Collins, S. J., & Brown, T. (2012). Convergence or business as usual?: Comparing new media use at television stations and newspapers. Atlantic Journal Of Communication, 20(4), 248-260. doi:10.1080/15456870.2012.711181
Dailey, L., Demo, L., & Spillman, M. (2005a). Most TV/newspapers partners at cross promotion stage. Newspaper Research Journal, 26(4), 36-49. Retrieved from http://content.ebscohost.com.lib.ezproxy.uakron.edu:2048/ContentServer.asp?T= P&P=AN&K=507835005&S=R&D=eft&EbscoContent=dGJyMMvl7ESeprE4zO X0OLCmr0yeqK5Ssq24SbKWxWXS&ContentCustomer=dGJyMPGvrkixr7VNu ePfgeyx44Dt6fIA
Dailey, L., Demo, L., & Spillman, M. (2005b). The convergence continuum: A model for studying collaboration between media newsrooms. Atlantic Journal Of Communication, 13(3), 150-168. doi:10.1207/s15456889ajc1303_2
Dijck, J. V. (2011). Tracing twitter: The rise of a microblogging platform. International Journal of Media & Cultural Politics, 7(3), 333-348. doi:10.1386/macp.7.3.333_1
Dupagne, M., & Garrison, B. (2006). The meaning and influence of convergence. Journalism Studies, 7(2), 237-255. doi:10.1080/14616700500533569
Farhi, P. (2009) The Twitter explosion. American Journalism Review, 31(3), pp 26-31. Retrieved from http://ajrarchive.org/Article.asp?id=4756
Ferguson, D. A., & Greer, C. F. (2011). Local radio and microblogging: How radio stations in the U.S. are using twitter. Journal Of Radio & Audio Media, 18(1), 33- 46. doi:10.1080/19376529.2011.558867
Franklin, B. (2014). The future of journalism. Journalism Studies, 15(5), 481-499. doi:10.1080/1461670X.2014.930254
Gahran, A. (June 21 2011). Social media is not the enemy of journalism, Pew report indicates. Knight Digital Media Center. Retrieved from http://archive.knightdigitalmediacenter.org/leadership_blog/comments/20110619_ social_media_is_not_the_enemy_of_journalism_pew_report_indicates/
45 Gibbs, W.J., & Bernas, R.S. (2009). Visual attention in newspaper versus TV-related news websites. Journal of Usability Studies. 4(4), 147-165. Retrieved from http://uxpajournal.org/visual-attention-in-newspaper-versus-tv-oriented-news- websites/
Goh, D., & Lee, C. (2011). An analysis of tweets in response to the death of Michael Jackson. Aslib Proceedings, 63(5), 432-444. doi:10.1108/00012531111164941
Gore, W. (2014). Print running: Newspapers innovating for new audiences. Index On Censorship, 43(3), 51-54. doi:10.1177/0306422014549148
Greer, C. F., & Ferguson, D. A. (2011). Using Twitter for promotion and branding: A content analysis of local television Twitter sites. Journal Of Broadcasting & Electronic Media, 55(2), 198-214. doi:10.1080/08838151.2011.570824
Greer, J. B., & Yan, Y. (2011). Newspapers connect with readers through multiple digital tools. Newspaper Research Journal, 32(4), 83-97. Retrieved from http://content.ebscohost.com.lib.ezproxy.uakron.edu:2048/ContentServer.asp?T= P&P=AN&K=70292454&S=R&D=sih&EbscoContent=dGJyMMvl7ESeprE4zO X0OLCmr0yeqK5Ss6i4SbSWxWXS&ContentCustomer=dGJyMPGvrkixr7VNu ePfgeyx44Dt6fIA
Hardy, J. (2010). Cross-media promotion. New York: Peter Lang
Hagman, J. (2012). Joining the twitter conversation. Public Services Quarterly, 8(1), 78- 85. doi:10.1080/15228959.2012.650564
Heo, Y., & Park, H. (2014). How are newspaper companies using social media to engage and connect with their audiences? Characteristics and forms of Korean newspapers' YouTube use. Quality & Quantity, 48(5), 2899-2914. doi:10.1007/s11135-013-9931-4
Hermida A. (2010). From TV to Twitter: How ambient news became ambient journalism. Media Culture Journal, 13(2) Retrieved from http://journal.media- culture.org.au/index.php/mcjournal/article/viewArticle/220
Hermida, A. (2013). #Journalism: Reconfiguring journalism research about Twitter, one tweet at a time. Digital Journalism, 1(3) 295-313. doi:10.1080/21670811.2013.808456.
Hermida, A., Fletcher, F., Korell, D., & Logan, D. (2012). Share, like, recommend. Journalism Studies, 13(5/6), 815-824. doi:10.1080/1461670X.2012.664430
Holcomb, J., Gottfrield, J., Mitchell, A. (2013 Nov 14). News use across social media platforms. Pew Research Journalism Project. Retrieved from http://www.journalism.org/2013/11/14/news-use-across-social-media-platforms/
46 Holton, A., & Lewis, S. (2011). Journalists, social media, and the use of humor on Twitter. The Electronic Journal of Communication, 21(1 & 2). Retrieved from http://www.cios.org/EJCPUBLIC/021/1/021121.html
Knight, M. (2012). Journalism as usual: The use of social media as a newsgathering tool in the coverage of the Iranian elections in 2009. Journal Of Media Practice, 13(1), 61-74. doi:10.1386/jmpr.13.1.61_1
Ko, J. J., Kwon, H. W., Kim, H. S., Lee, K. K., & Choi, M. Y. (2014). Model for Twitter dynamics: Public attention and time series of tweeting. Physica A, 404142-149. doi:10.1016/j.physa.2014.02.034
Kwak, H., Lee, C., Park, H., & Moon, S. (2010, April). What is Twitter, a social network or a news media?. In Proceedings of the 19th international conference on World wide web. ACM. Retrieved from http://an.kaist.ac.kr/~haewoon/papers/2010- www-twitter.pdf
Lachlan, K. A., Spence, P. R., Lin, X., Najarian, K. M., & Greco, M. D. (2014). Twitter Use During a Weather Event: Comparing Content Associated with Localized and Nonlocalized Hashtags. Communication Studies, 65(5), 519-534. doi:10.1080/10510974.2014.956940
Lasorsa, D. (2012). Transparency and other journalistic norms on Twitter. Journalism Studies, 13(3), 402-417. doi:10.1080/1461670X.2012.657909
Lasorsa, D. L., Lewis, S. C., & Holton, A. E. (2012). Normalizing Twitter. Journalism Studies, 13(1), 19-36. doi:10.1080/1461670X.2011.571825
Ma, Z., Sun, A., & Cong, G. (2013). On predicting the popularity of newly emerging hashtags in Twitter. Journal Of The American Society For Information Science & Technology, 64(7), 1399-1410. doi:10.1002/asi.22844
Marwick, A. E., & Boyd, D. (2010). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media & Society, 13(1), 114- 133. doi:10.1177/1461444810365313
Mayer, V. (2011). New television and media?. Television & New Media, 12(2), 95-100. doi:10.1177/1527476410396010
McFedries, P. (2007). All A-Twitter. IEEE Spectrum, 44(10), 84. Retrieved from http://ieeexplore.ieee.org.lib.ezproxy.uakron.edu:2048/stamp/stamp.jsp?tp=&arnu mber=4337670
McFedries, P. (2013). Hashtag, you're it [Technically Speaking]. IEEE Spectrum, 50(4), 24. doi:10.1109/MSPEC.2013.6481692
Messaging in a 2.0 World. (2007). Library Technology Reports, 43(5), 62-66. doi: 10.5860/ltr
47 Miller, T. (2000). Hullo television studies, bye-bye television? Television & New Media 1(1): 3–8. Retrieved from http://www.tobymiller.org/images/Media%20Studies/Media%20Studies- Sub%20cat.%20Consumerism&Transformation/Hullo%20TV,%20bye%20bye% 20tv.pdf
Millner, L. (2012). A little bird told me. Journal Of Mass Media Ethics, 27(1), 60-62. doi:10.1080/08900523.2012.644140
Moon, S.J., & Hadley, P. (2014). Routinizing a new technology in the newsroom: Twitter as a news source in mainstream media. Journal Of Broadcasting & Electronic Media, 58(2), 289-305. doi:10.1080/08838151.2014.906435
Murthy, D. (2011). Twitter: Microphone for the masses?. Media, Culture & Society, 33(5), 779-789. doi:10.1177/0163443711404744
Naaman, M., Becker, H., & Gravano, L. (2011). Hip and trendy: Characterizing emerging trends on Twitter. Journal Of The American Society For Information Science & Technology, 62(5), 902-918. doi:10.1002/asi.21489
Nielsen. (2013). Local Television Market Estimate. Retrieved from http://www.nielsen.com/content/dam/corporate/us/en/docs/solutions/measurement /television/2013-2014-DMA-Ranks.pdf
Nielsen. (2014 April 9). Television. Retrieved from http://www.nielsen.com/us/en/solutions/measurement/television.html
Olmstead, K., Mitchell, A., & Rosenstiel, T. (2011 May 9). Navigating news online. Pew Research Journalism Project. Retrieved http://www.journalism.org/2011/05/09/navigating-news-online/
Palser, B. (2009). Hitting the tweet spot. American Journalism Review 31(2), 54. Retrieved from http://ajrarchive.org/Article.asp?id=4737
Papacharissi, Z., & de Fatima Oliveira, M. (2012). Affective news and networked publics: The rhythms of news storytelling on #Egypt. Journal Of Communication, 62(2), 266-282. doi:10.1111/j.1460-2466.2012.01630.x
Parmelee, J. H. (2013). Political journalists and Twitter: Influences on norms and practices. Journal Of Media Practice, 14(4), 291-305. doi:10.1386/jmpr.14.4.291_1
Petrović, S., Osborne, M., McCreadie, R., Macdonald, C., Ounis, I., & Shrimpton, L. (2013, June). Can Twitter replace Newswire for breaking news?. In ICWSM 138- 10. Retrieved from http://eprints.gla.ac.uk/82566/1/82566.pdf
48 Pew Research (2012 Sept 27). In changing news landscape, even television is vulnerable. Retrieved from http://www.people-press.org/2012/09/27/in-changing-news- landscape-even-television-is-vulnerable/
Purcell, K., Rainee, L., Mitchell A., Rosenstiel, T., & Olmstead, K. (2010 March 10). Understanding the participatory news consumer. Pew Internet and American Life Project. Retrieved from http://www.pewinternet.org/2010/03/01/understanding- the-participatory-news-consumer/
Reich, Z. (2013). The impact of technology on news reporting: A longitudinal perspective. Journalism & Mass Communication Quarterly, 90(3), 417-434. doi:10.1177/1077699013493789
Richard, P. (Oct 27, 2008). Newspaper circulation continues to decline rapidly. New York Times. Retrieved from http://www.nytimes.com/2008/10/28/business/media/28circ.html?_r=0
Rosenberry, J. (2013). Circulation, population factor into social media use. Newspaper Research Journal, 34(3), 86-100. Retrieved from http://web.a.ebscohost.com.lib.ezproxy.uakron.edu:2048/ehost/pdfviewer/pdfview er?sid=1e79c41b-ba4a-4690-b68c- ac2f47d18145%40sessionmgr4003&vid=31&hid=4209
Schmierbach, M., & Oeldorf-Hirsch, A. (2012). A little bird told me, so I didn't believe it: Twitter, credibility, and issue perceptions. Communication Quarterly, 60(3), 317-337. doi:10.1080/01463373.2012.688723
Schrøder, K. C., & Steeg Larsen, B. (2010). The shifting cross-media news landscape.. Journalism Studies, 11(4), 524-534. doi:10.1080/14616701003638392
Schulte, B. (2009). The distribution review. American Journalism Review. Retrieved from http://ajrarchive.org/article.asp?id=4844
Shoemaker, P. J. (2006). News and newsworthiness: A commentary. Communications, 31(1), 105-111. Retrieved from http://jonathanstray.com/papers/News%20and%20Newsworthiness%20-- %20A%20Commentary.pdf
Small, T. A. (2011). What the hashtag?. Information, Communication & Society, 14(6), 872-895. doi:10.1080/1369118X.2011.554572
Stewart, D. (2013). When Retweets Attack: Are Twitter users liable for republishing the defamatory tweets of others?. Journalism & Mass Communication Quarterly, 90(2), 233-247. doi:10.1177/1077699013482913
Sung, M., & Hwang, J. (2014). Who drives a crisis? The diffusion of an issue through social networks. Computers In Human Behavior, 36246-257. doi:10.1016/j.chb.2014.03.063
49 Thornton, L., & Keith, S. M. (2009). From convergence to webvergence: Tracking the evolution of broadcast-print partnerships through lens of change theory. Journalism & Mass Communication Quarterly, 86(2), 257-276. Retrieved from http://www.academia.edu/3506704/From_convergence_to_Webvergence_Trackin g_the_evolution_of_broadcast- print_partnerships_through_the_lens_of_change_theory
Thurman, N., & Newman, N. (2014). The future of breaking news online? A study of live blogs through surveys of their consumption, and of readers' attitudes and participation. Journalism Studies, 15(5), 655-667. doi: 10.1080/1461670X.2014.882080
Twitter.com. The story of a tweet. Retrieved from https://about.twitter.com/what-is- twitter/story-of-a-tweet
Twitter.com. The Twitter glossary. Retrieved from https://support.twitter.com/articles/166337-the-twitter-glossary
Veenstra, A. S., Iyer, N., Hossain, M., & Park, J. (2014). Time, place, technology: Twitter as an information source in the Wisconsin labor protests. Computers In Human Behavior, 3165-72. doi:10.1016/j.chb.2013.10.011
Veglis, A. (2012). From cross media to transmedia Reporting in newspaper articles. Publishing Research Quarterly, 28(4), 313-324. doi:10.1007/s12109-012-9294-z
Waldherr, A. (2014). Emergence of news waves: A social simulation approach. Journal Of Communication, 64(5), 852-873. doi:10.1111/jcom.12117
Weeks, B. E., & Holbert, R. (2013). Predicting dissemination of news content in social media: A focus on reception, friending, and partisanship. Journalism & Mass Communication Quarterly, 90(2), 212-232. doi:10.1177/1077699013482906
Westerman, D., Spence, P. R., & Van Der Heide, B. (2012). A social network as information: The effect of system generated reports of connectedness on credibility on Twitter. Computers In Human Behavior, 28(1), 199-206. doi:10.1016/j.chb.2011.09.001
Wotzko, R. (2012). Newspaper Twitter: applied drama and microblogging. Research In Drama Education, 17(4), 569-581. doi:10.1080/13569783.2012.727627
Yong, H. (2012). Spreading the news. Index On Censorship, 41(4), 107-111. doi:10.1177/0306422012466807
Yu-Ru, L., Lazer, D., & Nan, C. (2013). Watching how ideas spread over social media. Leonardo, 46(3), 277. Retrieved from http://web.a.ebscohost.com.lib.ezproxy.uakron.edu:2048/ehost/pdfviewer/pdfview er?sid=1e79c41b-ba4a-4690-b68c- ac2f47d18145%40sessionmgr4003&vid=38&hid=4209
50
APPENDICES
51 APPENDIX A
NIELSEN LOCAL TELEVISION MARKETS
2013 - 2014 Designated Market Rank Market Area
1 New York 2 Los Angeles 3 Chicago 4 Philadelphia 5 Dallas-Ft. Worth 6 San Francisco-Oak-San Jose 7 Boston (Manchester) 8 Washington, DC (Hagrstwn) 9 Atlanta 10 Houston 11 Detroit 12 Phoenix (Prescott) 13 Seattle-Tacoma 14 Tampa-St. Pete (Sarasota) 15 Minneapolis-St. Paul 16 Miami-Ft. Lauderdale 17 Denver 18 Orlando-Daytona Bch-Melbrn 19 Cleveland-Akron (Canton) 20 Sacramento-Stkton-Modesto 21 St. Louis 22 Portland, OR 23 Pittsburgh 24 Raleigh-Durham (Fayetvlle) 25 Charlotte 26 Indianapolis 27 Baltimore 28 San Diego 29 Nashville 30 Hartford & New Haven 31 Kansas City
52 32 Columbus, OH 33 Salt Lake City 34 Milwaukee 35 Cincinnati 36 San Antonio 37 Greenvll-Spart-Ashevll-And 38 West Palm Beach-Ft. Pierce 39 Grand Rapids-Kalamazoo-B.Crk 40 Austin 41 Oklahoma City 42 Las Vegas 43 Harrisburg-Lncstr-Leb-York 44 Birmingham (Ann and Tusc) 45 Norfolk-Portsmth-Newpt Nws 46 Greensboro-H.Point-W.Salem 47 Albuquerque-Santa Fe 48 Jacksonville 49 Louisville 50 Memphis 51 New Orleans 52 Buffalo 53 Providence-New Bedford 54 Wilkes Barre-Scranton-Hztn 55 Fresno-Visalia 56 Little Rock-Pine Bluff 57 Richmond-Petersburg 58 Albany-Schenectady-Troy 59 Mobile-Pensacola (Ft Walt) 60 Tulsa 61 Knoxville 62 Ft. Myers-Naples 63 Lexington 64 Dayton 65 Charleston-Huntington 66 Roanoke-Lynchburg 67 Wichita-Hutchinson Plus 68 Flint-Saginaw-Bay City 69 Honolulu 70 Green Bay-Appleton 71 Tucson (Sierra Vista) 72 Des Moines-Ames 73 Spokane 74 Omaha 75 Springfield, MO 76 Toledo 77 Columbia, SC
53 78 Rochester, NY 79 Huntsville-Decatur (Flor) 80 Portland-Auburn 81 Paducah-Cape Girard-Harsbg 82 Shreveport 83 Madison 84 Champaign & Sprngfld-Decatur 85 Syracuse 86 Harlingen-Wslco-Brnsvl-McA 87 Chattanooga 88 Waco-Temple-Bryan 89 Colorado Springs-Pueblo 90 Cedar Rapids-Wtrlo-IWC&Dub 91 El Paso (Las Cruces) 92 Savannah 93 Baton Rouge 94 Jackson, MS 95 Charleston, SC 96 South Bend-Elkhart 97 Tri-Cities, TN-VA 98 Burlington-Plattsburgh 99 Greenville-N.Bern-Washngtn 100 Davenport-R.Island-Moline 101 Ft. Smith-Fay-Sprngdl-Rgrs 102 Myrtle Beach-Florence 103 Johnstown-Altoona-St Colge 104 Evansville 105 Lincoln & Hastings-Krny 106 Tallahassee-Thomasville 107 Reno 108 Tyler-Longview(Lfkn&Ncgd) 109 Ft. Wayne 110 Boise 111 Sioux Falls(Mitchell) 112 Augusta-Aiken 113 Youngstown 114 Springfield-Holyoke 115 Lansing 116 Fargo-Valley City 117 Peoria-Bloomington 118 Macon 119 Traverse City-Cadillac 120 Montgomery-Selma 121 Eugene 122 Lafayette, LA 123 SantaBarbra-SanMar-SanLuOb
54 124 Yakima-Pasco-Rchlnd-Knnwck 125 Monterey-Salinas 126 Columbus, GA (Opelika, AL) 127 Bakersfield 128 La Crosse-Eau Claire 129 Corpus Christi 130 Amarillo 131 Wilmington 132 Chico-Redding 133 Columbus-Tupelo-W Pnt-Hstn 134 Topeka 135 Wausau-Rhinelander 136 Rockford 137 Monroe-El Dorado 138 Columbia-Jefferson City 139 Duluth-Superior 140 Medford-Klamath Falls 141 Beaumont-Port Arthur 142 Salisbury 143 Lubbock 144 Wichita Falls & Lawton 145 Minot-Bsmrck-Dcknsn(Wlstn) 146 Anchorage 147 Sioux City 148 Palm Springs 149 Erie 150 Odessa-Midland 151 Albany, GA 152 Joplin-Pittsburg 153 Rochestr-Mason City-Austin 154 Panama City 155 Terre Haute 156 Bangor 157 Wheeling-Steubenville 158 Bluefield-Beckley-Oak Hill 159 Binghamton 160 Biloxi-Gulfport 161 Sherman-Ada 162 Idaho Fals-Pocatllo(Jcksn) 163 Gainesville 164 Missoula 165 Abilene-Sweetwater 166 Yuma-El Centro 167 Hattiesburg-Laurel 168 Billings 169 Clarksburg-Weston
55 170 Quincy-Hannibal-Keokuk 171 Utica 172 Dothan 173 Rapid City 174 Elmira (Corning) 175 Lake Charles 176 Watertown 177 Jackson, TN 178 Harrisonburg 179 Alexandria, LA 180 Marquette 181 Jonesboro 182 Bowling Green 183 Charlottesville 184 Laredo 185 Grand Junction-Montrose 186 Meridian 187 Lima 188 Butte-Bozeman 189 Lafayette, IN 190 Greenwood-Greenville 191 Great Falls 192 Twin Falls 193 Bend, OR 194 Parkersburg 195 Eureka 196 Cheyenne-Scottsbluff 197 Casper-Riverton 198 San Angelo 199 Mankato 200 St. Joseph 201 Ottumwa-Kirksville 202 Fairbanks 203 Victoria 204 Zanesville 205 Helena 206 Presque Isle 207 Juneau 208 North Platte 209 Alpena 210 Glendive
56 APPENDIX B
MEDIA MARKETS AND NEWS ORGANIZATIONS
Market Size City, State Local Newspaper Local TV News Station
2 Los Angeles, CA KCBS KCAL Westside Today 14 Tampa, FL WFLA Tampa Tribune 15 Minneapolis, MN KMSP Business Journal 23 Pittsburg, PA KDKA Pittsburg Business Times 28 San Diego, CA KFMB San Diego Union Tribune 32 Columbus, OH WCMH This Week 36 San Antonio, TX KABB San Antonio Current 37 Greenville, NC WCTI WFXI The Daily Reflector 44 Birmingham, AL WIAT Birmingham Times 47 Albuquerque, NM KRQE Albuquerque Journal 60 Tulsa, OK KJRH Tulsa World 67 Wichita, KS KSN Wichita Eagle 68 Flint, MI WJRT Flint Journal 69 Honolulu, HI KHON Hawaii Reporter 73 Spokane, WA KXLY Spokesman-Review 76 Toledo, OH WNWO Toledo Free Press 83 Madison, WI WMSN Capital Times 86 Harlingen, TX KGBT Valley Morning Star 93 Baton Rouge, LA WBRZ The Advocate 95 Charleston, SC WTAT Post & Courier 105 Lincoln, NE KLKN Lincoln Journal Star 121 Eugene, OR KMTR Register Guard 136 Rockford, IL WREX Rock River Times 155 Terre Haute, IN WTWO Tribune Star 169 Clarksburg, WV WBOY Exponent Telegram 177 Jackson, TN WBBJ Jackson Sun 199 Mankato, MN KEYC Free Press 203 Victoria, TX KAVU Victoria Advocate 207 Juneau, AK KJUD Juneau Empire 209 Alpena, MI WBKB Alpena News
57 APPENDIX C
NEWS ORGANIZATION TWEETS CODING SHEET
1. Case Number ______2. Coder I.D. ______3. City, State of News Organization ______4. Type of News Organization ______(TV Program=1, Newspaper=2) 5. Name of News Organization ______6. News Market Size ______large (1-50)=1, medium (51-100)=2, small(100+)=3 7. Day of Week ______MON=1, TUES=2, WED=3 THURS=4, FRI=5, SAT=6, SUN=7 8. Total Number of Tweets ______9. Total Number of Followers ______10. News Organization Twitter Username ______11. Time of Tweet ______1AM=1 2AM=2 3AM=3 4AM=4 5AM=5 6AM=6 7AM=7 8AM=8 9AM=9 10AM=10 11AM=11 12PM=12 1PM=13 2PM=14 3PM=15 4PM=16 5PM=17 6PM=18 7PM=19
58 8PM=20 9PM=21 10PM=22 11PM=23 12AM=24 12. Tweet Structure (Yes=1, No=0) Tweet with @username ______Tweet with Hashtag (#) ______Tweet with Photo or Video Attached ______Tweet with Link Attached ______13. Tweet with Audience Engagement Number of Retweets ( ) ______Number of Favorites (★) ______14. Tweet News Content (Yes=1, No=0) Tweet about Breaking News ______Tweet about Business ______Tweet about Crime ______Tweet about Education ______Tweet about Entertainment ______Tweet about Health ______Tweet about Human Interest ______Tweet about Politics ______Tweet about Sports ______Tweet about Traffic ______Tweet about Weather ______Other ______15. Tweet Topics (Yes=1, No=0) Tweet Calls for Action ______Tweet Expresses Emotion ______Tweet Interacts with Audience ______Tweet Shares Information ______16. Tweet Promotion Activity (Yes=1, No=0) Tweet with General Organization Promotion______Tweet with Strategic Promotion ______Tweet with Targeted Audience Promotion ______
59 APPENDIX D
NEWS ORGANIZATION TWEETS CODING BOOK
This study is going to compare Twitter use of local newspapers and local television news stations. The researcher wants know which news organization generates the most Twitter activity and what kind of Twitter content is produced for online audiences, as well as how news organizations engage their online audience. Tweets retrieved from Twitter.com will be used to test the research questions. The following are instructions for the coding procedure. Using tweets composed during November 1 through November 30, a sheet will be completed for each tweet composed by local newspapers and local television news stations.
1. Case Number: A case number will be assigned to each tweet.
2. Coder I.D.: Coding identification numbers will be assigned to each coder.
(Coder 1=1, Coder 2=2, Coder 3=3)
3. City, State of News Organization: City and Station will correspond with the news
organization location.
4. Type of News Organization: Coders will indicate whether the tweet is composed
by a television news program or newspaper (TV news program=1, Newspaper=2).
5. Name of News Organization: The researcher will provide a comprehensive list of
news organization information including the name. The researcher will gather this
60 information from USNPL, a media directory with links of local newspapers and
local television stations: http://www.usnpl.com.
6. News Market Size: The news market size will be included in the list provided to
the coders. Coders will identify the size of market as large=1, medium=2,
small=3. Coders will code 1 for markets 1-50. Coders will code 2 for markets 51-
100. Coders will code 3 for markets 100+.
7. Day of the Week: Day of the week will be coded as Monday to Sunday. The day
of the week will be based on what day each tweet was composed on. Coders will
get this information from the date listed on each tweet. Coders will also be
provided a calendar, in order to code the day to correspond with the date of the
tweet (Monday=1, Tuesday=2, Wednesday=3, Thursday=4, Friday=5,
Saturday=6, Sunday=7).
8. Total Number of Tweets: Coders will get this information from the Twitter
homepage of each news organization.
Example: The Twitter homepage of KTLA shows 66.6K tweets.
9. Total Number of Followers: Coders will get this information from the Twitter
homepage of each news organization.
Example: The Twitter homepage of KTLA shows 143K followers.
61
10. News Organization Twitter Username: Coders will get this information from the
Twitter homepage of news organization. The username is the Twitter user that
composes the tweets. The username begins with the @ symbol.
Example: The Twitter homepage of KTLA shows the Twitter Username is
@KTLA.
11. Time of Tweet: Time of each tweet will be coded based on the 24-hour time
clock. Coders will indicate 1, 2, 3, and so on (1AM=1, 2AM=2, 3AM=3, 4AM=4,
5AM=5, 6AM=6, 7AM=7, 8AM=8, 9AM=9, 10AM=10, 11AM=11, 12PM=12,
1PM=13, 2PM=14, 3PM=15, 4PM=16, 5PM=17, 6PM=18, 7PM=19, 8PM=20,
9PM=21, 10PM=22, 11PM=23, 12AM=24) based on what time of day each tweet
62 was composed. Coders will get this information from the time stamp on each
tweet.
Example: The tweet below was composed at 1:23pm. Coders will indicate 13 for
time of tweet.
12. Tweet Structure: The structure of the tweet will be coded based on what is
included in each Twitter post. Coders will indicate 1 for Yes or 0 for No for each
element of content: @usernames, hashtags, photos or videos and links.
-A username will have the @ symbol in front of a Twitter user.
-A hashtag will be a phrase with the # symbol is front on it.
-A photo or video will be attached to the tweet.
-A link will be attached to the tweet.
Example: The tweet below has a hashtag, link, and photo attached. Coders will
indicate 1 for hashtag, link, and photo and 0 for username.
63 13. Tweet with Audience Engagement: Audience engagement will be coded based on
how many retweets ( ) and favorites (★) a tweet receives. Coders will provide
the number of each.
-A tweet will indicate if it has been retweeted if there is a number indicated by the
square arrow symbol ( ).
-A tweet will indicate if it has been favorited if there is a number by the star
symbol (★).
Example: The tweet below has been retweeted 7 times and favorited 7 times.
Coders will indicate 7 for retweets and 7 for favorites.
14. Tweet News Content: The news content will be coded based on what type of news
story in posted in a tweet. Coders will indicate 1 for Yes and 0 for No based on
the category of news content: Breaking, Business, Crime, Entertainment, Health,
Human Interest, Traffic, and Weather.
-Breaking news should be coded if a tweet contains new information regarding an
event that is currently occurring or developing. Breaking news usually refers to
events that are unexpected, such as a plane crash or building fire. Breaking news
is generally bad news; crime, political conflict, threats to the health of the public,
64 sex scandals, war, and death. A hard or breaking news story is comparable to
hearing about a car crash. The bad news comes first and then later news stories
have developing aspects of the event (Shoemaker, 2006, p. 107).
-Business news should be coded if a tweet contains information regarding the
economy, stock market, finance, investing, etc.
-Crime news should be coded if a tweet contains information regarding missing
person cases, murder, illegal drugs, trials, arrests, etc.
-Education news should be coded if a tweet contains information regarding
academics, including colleges & universities, teachers, public & private schools,
tuition, scholarships, financial aid & student loans.
-Entertainment news should be coded if a tweet contains information regarding
pop culture, such as celebrities, movies, music, etc.
-Health news should be coded if a tweet contains information regarding medicine,
fitness, nutrition, aging, etc.
-Human Interest news should be coded if a tweet contains information regarding
news that feature people, events, or issues to engage emotion.
-Politics news should be coded if a tweet contains information regarding
elections, Congress, Capital Hill, lobbying, or advocacy.
-Sports news should be coded if a tweet contains information regarding NFL,
MLB, NBA, College or high school sports, scores, or team schedules.
-Traffic news should be coded if a tweet contains information regarding road
conditions and travel.
65 -Weather news should be coded if a tweet contains information regarding weather
forecasts or severe weather.
If a tweet contains more than one news category, codes will give 1 for all
categories that apply.
Example: The tweet below would be coded as breaking and crime news. Coders
will indicate 1 for breaking and crime and 0 for business, entertainment, health,
human interest, traffic and weather.
14. Tweet Topic: The tweet topic will be coded based on the goal of the tweet. Coders
will indicate 1 for Yes and 0 for No based on the each topic of the tweet: Calls for
action, Expresses emotion, Interacts with audience, Shares Information.
-Calls for action will be coded if the tweet is telling Twitter users to do something.
Retweeting is considered a call for action.
-Expresses emotion will be coded if the tweet describes the mood or attitude of the
news organization.
-Interacts with audience will be coded if a tweet is connecting or communicating
with other twitter users. If the news organization directly mentions followers or
asks followers a question, this is considered interacting.
-Shares information will be coded if a tweet is providing general news information
to other users.
66 If a tweet contains more than one tweet topic, coders will give 1 for all categories that apply.
Example: The tweet below should be coded as shares information. Coders will
indicate 1 for shares information and 0 for calls for action, expresses emotion and
interacts with audience.
15. Promotional Activity of Tweet: The promotional activity of the tweet is be coded
based on the goal of the tweet. Coders will indicate 1 for Yes and 0 for No based
on each promotion of the tweet: General Organization Promotion, Strategic
Promotion, and Targeted Audience Promotion.
-General Organization Promotion will be coded if the tweet generally promotes the
news organization.
-Strategic Promotion will be coded if the tweet promotes contests or contest
winners, promotions for organizations or events outside of news organization.
-Targeted Audience Promotion will be coded if the tweet reaches out to the
audience by promoting on-air or online programming.
Example: The tweet below should be coded as targeted audience promotion
because it is promoting on-air programming. Coders will indicate 1 for targeted
audience promotion and 0 for general station promotion and strategic promotion.
67