Gaining Primacy in the Digital Network

Accessed 13 Jun 2013 13:06 GMT GMT

Gaining Primacy in the Digital Network Using Social Network Analysis to Examine Sports Journalists’ Coverage of the Penn State Football Scandal via Twitter MAR ION E . HAMBR ICK AND JIMMY SANDERSON

This study explored the social network that developed amongst sports journalists as the Penn State football scandal evolved. The results suggest that Twitter is an essential domain for sports journalists to occupy when reporting sports stories. Further as sports journalists gain prominence in Twitter networks, they gain exposure to large audiences, thereby obtaining a prime agenda-setting position. Twitter has become a vital tool in sports journalists’ repertoire and cannot be ignored.

n November 4, 2011, Bill Rabinowitz of the Columbus Dispatch posted a message on Twitter pertaining to Pennsylvania State University: “Huge news at Penn State. Former longtime football asst coach Sandusky indicted on various sex charges.” Messages about this story quickly escalated as Penn State found itself embroiled in a child sex abuse scandal. Grand jury testimony revealed multiple abuse accusations against former football defensive coordinator Gerald “Jerry” Sandusky (Chappell, 2011). One particular charge encompassed current assistant coach Mike McQueary (a graduate assistant at the time) who witnessed Sandusky engage in sexual activity with a young boy in the football facility’s showers. McQueary reported this incident to head football coach Joe Paterno, who subsequently informed Athletic Director Tim Curley and Vice-President of Finance and Business Gary Schultz. Yet, neither Curley nor Schultz notified law enforcement. As these details became public, the Penn State Board of Trustees fi red Paterno, Curley, and Schultz. The latter two individuals now face criminal charges for failing to report the incident and lying to the grand jury (Chappell, 2011). Rabinowitz followed-up his initial tweet with another salvo on November 5, 2011, “Penn State scandal grows. AD Tim Curley charged with per-

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jury.” Sports Illustrated’s college football report Stewart Mandel reposted this message with a retweet1 stating: “Wow. RT @brdispatch: Penn State scandal grows. AD Tim Curley charged with perjury.” Yahoo! Sports Pat Forde then joined in the growing Twitter commentary: “Not good. RT @ slmandel Wow. RT @brdispatch: Penn State scandal grows. AD Tim Curley charged with perjury.” In turn, Dana O’Neil of ESPN posted a one-word reply to Forde: “Speechless. RT @YahooForde: Not good. Penn State scandal grows. AD Tim Curley charged with perjury.” This message chain demonstrates the ease and speed that information is exchanged via Twitter. These four journalists used Twitter parlance and abbreviated speech to convey both pertinent information and commentary about the growing Penn State scandal. Within a short time period, these sports journalists joined a larger number of their colleagues who also turned to Twitter to weigh in on this story over the fi rst two weeks of the scandal from November 4–18, 2011. Their tweets provided, among others, information about Penn State’s response, speculation on Joe Paterno’s tenure and legacy, and details about the Penn State student body’s reactions (Daniels, 2011). Twitter played a vital role in the collection and dissemination of news during this tumultuous period, and the Penn State scandal offers a vivid example of the expanding, and arguably, essential, role of social media in sports reporting. This study builds on prior research on sports journalists Twitter use (Schultz & Sheffer, 2010; Sheffer & Schultz, 2010), by exploring how a Twitter network of sports journalists formed and changed as the Penn State story evolved. Literature Review The Changing Media Landscape Researchers have observed how shifts in the media industry from a heavy reliance on traditional formats to a greater integration of social media such as blogs and Twitter (Sanderson & Kassing, 2011; Schultz & Sheffer, 2010; Sheffer & Schultz, 2010). Researchers have examined mainstream media organizations’ social media use (Meraz, 2009; Park, 2003; Weber, 2012) as well as specific journalists’ Twitter use (An, Cha, Gummadi, & Crowcraft, 2011; Cheng, Sun, Hu, & Zeng, 2011; Kwak, Lee, Park, & Moon, 2010). Through social media, both mainstream media organizations and journalists document thoughts, ideas, and opinions in a format that invites audience commentary. For instance, Meraz (2009) investigated how the New York Times and 2

Journal of Sports Media, Vol. 8, No. 1, Spring 2013

Washington Post used blogs to disperse news beyond print publications via hyperlinks to additional online content about news stories. Meraz posited that hyperlinks strengthened mainstream media’s facilitative capabilities by directing readers to other prominent news. Similarly, Weber (2012) analyzed how newspaper organizations employed hyperlinks to connect with other newspapers and blogs, gestures that were then reciprocated. Weber observed that this symbiotic hyperlinking enabled newspapers and blogs to increase their reach and influence. This research demonstrates how media outlets leverage digital resources to build connections and amplify the quantity of information available to readers. Whereas blogs have induced significant alterations to news processes, these changes are magnified by the influence of social media, in particular Twitter, on the news cycle. Twitter and News Twitter has become a viable player in news industry (Arceneaux & SchmitzWeiss, 2010). Twitter functions as a “microblog,” allowing users to share information through posts termed “tweets” that are limited to 140 or fewer characters. Similar to text messaging, Twitter enables users to post and access tweets through both computers and mobile devices. Most tweets are public in nature, allowing anyone who “follows,” or subscribes to that user’s tweets, to read and comment on these messages. The relatively open access of Twitter promotes rapid production and dissemination of information to broad audiences. Accordingly, Twitter’s brevity and speed provide advantages over traditional media formats with respect to reporting and accessing breaking news (Arceneaux & Schmitz-Weiss, 2010). Twitter’s interactive features have escalated its popularity among diverse audiences, including both news producers and consumers (Cheng et al., 2011) and researchers are paying closer attention to Twitter’s role in the news cycle. Kwak et al. (2010) found that celebrities and major media outlets had the largest Twitter followings, with most content centering on headline news and sports. Yet they also noted that tweets had brief lifespans, losing relevancy within one day. They also discovered that Twitter users obtained news primarily through re-tweets that reached an average of 1,000 users and were typically re-transmitted within one hour of the initial posting. News organizations with the most re-tweets included ESPN, Huffington Post, and NPR, which enabled these media outlets to form a “collective intelligence,” as this news was re-distributed via legions of Twitter users (Kwak et al., 2010, p. 8). Similarly, An et al. (2011) investigated the prominence of specific media organizations (e.g., CNN, ESPN, The New Hambrick & Sanderson: Gaining Primacy in the Digital Network

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York Times) on Twitter. The voluminous following these organizations enjoyed allowed them to dictate information spread and user consumption as tweets that included links to the organization’s website were re-tweeted an average of 15.5 times. The researchers observed that “Twitter in this case acted as an echo-chamber” (p. 3), as the same tweets and information quickly percolated. Clearly established media organizations have an influential role on Twitter, yet this presence is achieved by the participation of its journalists. This trend has become particularly salient with sports journalists. Similar to their contemporaries, sports journalists use Twitter as an information source (e.g., citing an athlete’s tweet in a story) and platform for information sharing (e.g., tweeting about an upcoming event) (Hutchins, 2011; Sanderson, 2011). Schultz and Sheffer (2010) examined sports journalists Twitter use through a two-part analysis. In their fi rst study, they surveyed print and broadcast journalists to ascertain their rationale for using Twitter. Interview responses yielded five categories: (a) breaking news; (b) providing personal opinions; (c) promoting media outlets; (d) connecting with fans and others; and (e) becoming better journalists. In a follow-up study, Sheffer and Schultz (2010) employed content analytic methods to unpack sports journalists’ tweets and determine if their actual Twitter use mirrored their reported reasons for using Twitter. The results revealed marked differences as sports journalists utilized Twitter more frequently to provide personal commentary and opinions rather than break news and promote organizations. These confl icting results prompted Sheffer and Schultz (2010) to call for more research on sports journalists Twitter use, a task that this research undertakes via social network analysis. Social Network Analysis and Twitter Twitter is a potent communications channel for sports journalists. Bruns and Burgess (2011) observed that Twitter possesses immense potential for sports journalists and sports consumers to share and obtain information. Bruns and Burgess posited that Twitter is a blossoming research area and subsequently identified several areas for future exploration including: (a) identifying and tracking the most popular Twitter users based on number of followers and tweets; and (b) trending topics and frequently used keywords. Such efforts will unpack what information resonates with Twitter users, how far Twitter users extend conversations on these topics, and the formal/informal communities that develop within Twitter-directions that lend themselves to social network analysis. 4


Social network analysis allows researchers to examine communities, or social networks, composed of individuals, groups, and the shared relationships among them (Wasserman & Faust, 1994). This analytical approach derives from sociology and anthropology, enabling researchers to explain how social network members came together, how relationships form among them, and what information and resources are exchanged via network relationships. Researchers employ a combination of statistics and diagrams, or sociograms, to quantify the network’s structure, individual relationships, and qualitative descriptions of members’ positions in the network (Wasserman & Faust, 1994). Social network analysis is gaining more traction in sports research. For example, Quatman and Chelladurai (2008) used sociograms to analyze sport management research and publication trends. They found that a core group of researchers had a significant influence on the overall academic social network, as their published studies influenced the field’s subsequent research areas. MacLean, Cousens, and Barnes (2011) examined network relationships among community basketball clubs, a nonprofit multisport recreation organization, and two university athletic departments. Their social network analysis revealed the community basketball clubs shared close relationships with one another but looser relationships with the recreation organization and athletic departments, an outcome that limited the larger basketball community’s ability to leverage its partnerships and provide quality sport delivery. Warners, Bowers, and Dixon (in press) studied two collegiate women’s basketball teams and the shared relationships among coaches, players, and staff members. A series of network sociograms revealed shifts in team dynamics over time. Coaches played a central role early in the season, whereas players and staff members assumed more central roles later in the season and tightly-knit networks were more likely to be successful. Finally, Hambrick (2012) examined how bicycle race organizers used Twitter to promote events. The sociograms revealed that Twitter users joined each race organizer’s informal network within the fi rst four days of the network’s creation, and then helped race organizers increase their reach as they solicited participants and spectators. The aforementioned studies demonstrate the application of social network analysis in sports research, yet more work is needed. Park (2003) called for researchers to employ social network analysis to study news organizations. He argued that this approach would shed light on how they connect to one another, how and what type of communication flows emerge from the connections, and how news organizations leverage these Hambrick & Sanderson: Gaining Primacy in the Digital Network

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exchanges to share information and enhance offl ine conversations. When Twitter users follow another person, group, or organization, they confer a level of importance on that user and indicate an interest in reading their tweets. Sports journalists, like other Twitter users, follow a diverse range of Twitter users, including their peers. In that vein, this research utilized social network analysis to explore how networks amongst sports journalists developed and changed as the Penn State story evolved. The present study was guided by the following research questions. RQ1.

Who were the top sports journalists using Twitter to discuss the Penn State story?

RQ2.

How did the sports journalists interact, and how did their interactions evolve?

RQ3.

Which sports journalists played a prominent role in the Twitter discussion?

Method Data Collection Top sports journalists were determined by the number of their contemporaries who followed them. These journalists worked for major media outlets in television (e.g., ESPN, Fox Sports), radio (e.g., Paul Finebaum Radio, The Jim Rome Show), magazines and trade publications (e.g., Sports Illustrated, Sports Business Journal), newspapers (e.g., Wall Street Journal, Los Angeles Times), local media outlets (e.g., Lexington Herald-Leader), and blogs (e.g., Deadspin, Quickish). One of the authors fi rst learned about the Penn State story through a tweet posted by ESPN’s Dana O’Neil (mentioned in the introduction) responding to previous messages by fellow journalists Pat Forde, Stewart Mandel, and Bill Rabinowitz. O’Neil’s tweet initiated the data collection process via snowball sampling. Snowball sampling represents “an approach for locating information-rich key informants or critical cases” (Patton, 2002, p. 237), and was used to identify the top journalists discussing the Penn State case via Twitter. Starting with the four journalists above, the researchers went to each journalist’s Twitter home page to identify who they followed and who followed them. In November 2011, the four journalists followed a combined 2,321 Twitter users. Some users were followed by all four journalists, while others were followed by only one or two of the journalists. The researchers selected users followed by at least three journalists, indicating 6


consensus about that user’s relative importance. Any non-journalists (e.g., Barack Obama, Stephen Colbert) were removed from further analysis. Through this process, the researchers culled the list of 2,321 users to 146 sports journalists. The researchers used this shortened list and initiated the process again by examining who these journalists followed and who followed them. This iterative process was completed four times resulting in data from 236 sports journalists who had a combined 102,342 followers. Through snowball sampling, “the chain of recommended informants would typically diverge initially as many possible sources are recommended, then converge as a few key names get mentioned over and over” (Patton, 2002, p. 237). The researchers converged the sample to identify the “key names” resulting in the top 151 sports journalists, who were followed by 3 to 42 other sports journalists in the sample. Tweets posted by these 151 journalists during a two-week period from November 4–18, 2011 were downloaded. The time period included several key stories such as the Penn State Board of Trustees decision to fi re head football coach Joe Paterno and President Graham Spanier, and Jerry Sandusky’s prime-time interview with Bob Costas. This data was used in conjunction with the sports journalists’ follower information to assess the informal social network as sports journalists weighed in on the Penn State discussion. The journalists wrote 62,742 tweets during this period of which 6,770 focused on the Penn State story. Data Analysis Social network analysis was used to explore how sports journalists interacted with one another as they used Twitter to cover the Penn State story. Each journalist’s follower data helped identify the informal social network members, their relationships with fellow members, and how they used these relationships to share information within the informal network (Wasserman & Faust, 1994). Sociograms were employed to depict network members and relationships. A single point, or node, represents a network member, and a line drawn between two nodes indicates a shared relationship between two members, ultimately displaying the complete collection of nodes and relationships within the social network. Researchers also create sociograms to denote various points in time, and these diagrams provide insights into network evolution, such as when members joined the network and how they interacted over time (de Nooy, Mrvar, & Batageli, 2005). The Pajek (PAY-yek) software program was used to visually depict the Hambrick & Sanderson: Gaining Primacy in the Digital Network

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sports journalists’ social network. A series of sociograms were created for the two-week period to show a time-elapsed display of the informal network’s growth along with the number of new journalists and follower relationships added daily to the network. Social network statistics also were employed to quantitatively describe the network’s evolution. These statistics included means and standard deviations plus frequency tables to quantify the average number of journalists and follower relationships added to the network each day. Pajek also was used to quantify the network’s density-the ratio between the actual network follower relationships versus the maximum number possible. Density values range from zero to one, where .000 indicates no follower relationships among network members, whereas 1.000 reveals all members share direct connections. Typically, as the number of network members increases, the network’s density value decreases, simply because members have a limited ability to form and maintain follower relationships. Higher density values indicate that information and resources potentially flow more quickly and efficiently through a network, while lower values indicate a slower and less resourceful flow (de Nooy et al., 2005). In this study, density values closer to .000 indicate few journalists shared relationships within the informal network, whereas values closer to 1.000 suggest that journalists shared numerous relationships. Fewer relationships would limit the flow of information among the journalists, while more relationships would increase the speed and ease of information dissemination within the network and beyond. Follower numbers were used to assess each journalist’s relative network position. These numbers indicated how many people subscribed to the journalist’s tweets, aligning with research that suggests Twitter users with sizeable followers dictate how and what type of information is spread (Malinick et al., 2011). Sociograms were employed to ascertain the influential members within the network. Members with more influence often share numerous network relationships and reside at or near the network’s center. Conversely, members with less influence have fewer relationships and often congregate along the outskirts of the network. Sociograms depict how information and resources are shared among influential members and their less influential counterparts. Lines connecting two network members reveal interactions between the members and an ongoing opportunity for sharing (de Nooy et al., 2005). Journalists located at the network’s center would have more shared follower relationships and serve as information hubs, disseminating information to other journalists. Journalists stationed along the network’s periphery would have fewer relationships, and this network location limits information 8


gathering and sharing efforts. Sociograms in conjunction with follower information provided insights into the most influential journalists in the network during this story. Results RQ1. Who were the top sports journalists using

Twitter to discuss the Penn State story? Pete Thamel of The New York Times had the most network followers with 42 of the 151 following him, while Newsday’s Bob Glauber had the fewest with three. The sports journalists worked in various media outlets: television (66–43%), newspapers (35– 23%), magazines (22–15%), blogs (20–13%), radio (3– 2%), newswire services (3– 2%), and other (2–1%). ESPN had the largest contingency with 48 journalists, followed by Sports Illustrated with 19 journalists. CBS had eight journalists, and Yahoo! Sports and New York Times each had six journalists. The Los Angeles Times had four journalists, while the Associated-Press, FOX Sports, Sports Business Journal, and Sporting News had three journalists each. Most of the remaining outlets had no more than one or two journalists representing their organizations. The majority of journalists were men (117– 77%) versus women (14– 9%). Media outlets such as Deadspin, Grantland.com, and Wall Street Journal Sports represented the remaining “journalists” (20–13%). RQ2. How did the sports journalists interact, and

how did their interactions evolve? The 151 journalists joined the informal social network from November 4–18 (Table 1). Twelve journalists joined on day one when the news fi rst broke, and they shared 43 relationships within the network (Figure 1). The following day, an additional 42 journalists joined for a total of 54 journalists, and they shared 585 relationships (Figure 2). The number of new journalists tapered off after the second day with 14 joining the third day and 22 the fourth day. By day six, when the Board of Trustees fi red Paterno and Spanier, 143 journalists, or 95% of them, had joined the network, and they shared 2,445 relationships. The last journalist joined on day 11, when Costas interviewed Sandusky. On this day, the network contained 151 journalists who shared 2,777 relationships (Figure 3), and the number of journalists and relationships remained static through the fi nal day of the twoweek period. Hambrick & Sanderson: Gaining Primacy in the Digital Network

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table 1. Network journalists, relationships, and density with timeline of events DAY

NETWORK

NETWORK

NETWORK

JOURNALISTS

RELATIONSHIPS

DENSITY

1 2 3 4

12 54 68 90

43 585 819 1,086

0.299 0.201 0.177 0.134

5 6 7 8 9 10 11 12 13 14 15

122 143 149 150 150 150 151 151 151 151 151

1,761 2,445 2,579 2,699 2,699 2,699 2,777 2,777 2,777 2,777 2,777

0.118 0.120 0.116 0.120 0.120 0.120 0.122 0.122 0.122 0.122 0.122

TIMELINE OF EVENTS

Breaking news Curley, Schultz vacate positions Curley, Schultz charged with perjury Paterno and Spanier fired McQueary placed on leave

Bob Costas interviews Sandusky

The mean number of journalists joining the network each day was 10.07 (SD = 13.55) with a mean of 185.13 new relationships added per day (SD = 249.21). The majority of the journalists joined within the fi rst six days, tapering off during the fi nal days. The mean number of journalists joining the fi rst six days was 23.83 (SD = 11.36) compared to a mean of .89 (SD = 1.96) each day for the remaining time period. Similarly, the mean number of relationships added in the first six days was 407.50 (SD = 264.12) versus a mean of 36.89 (SD = 57.22) for the remaining days. The journalists posted 6,770 Penn State-related tweets during this time period. The fi rst day they posted 12 tweets, and the number increased to 243 tweets the second day. The journalists posted the most messages on days five and six with 1,441 and 1,163 tweets, respectively. The average number of tweets posted daily was 451.33 (SD = 425.06). The mean number of tweets was 557.00 (SD = 565.52) for the fi rst six days and 380.89 (SD = 319.92) for the remaining days. The network sociograms and density values demonstrate the network’s rapid expansion. The first day’s sociogram showed a sparse network of 12 members and 43 relationships among them. Four network members comprised the network’s core, and they shared relationships with the other 10


FIG. 1. Sociogram of the sports journalist social network on day 1.

FIG.

2. Sociogram of the sports journalist social network on day 2.

3. Sociogram of the sports journalist social network on day 15.

FIG

members. Two members shared no relationships within the network and remained on the network’s periphery. The network’s density was .299 on the fi rst day, indicating the network members shared close to 30% of the relationships possible within the network. On the second day, the largest increase in network membership occurred, when an additional 42 journalists joined the network and added 547 new relationships. The network’s density then decreased to .200, as members shared approximately 20% of the possible relationships with other members. By the sixth day, the network contained the vast majority of network members and relationships with 143 journalists and 2,445 relationships among them. The sociogram revealed multiple relationships between journalists at the network’s core. However, many journalists remained along the periphery, connecting to as few as three or four other members. The network’s density ultimately decreased to .122, indicating the journalists shared just over 12% of the relationships possible within the network. RQ3. Which sports journalists played a prominent

role in the Twitter discussion? As of November 2011, the sports journalists followed an average of 478 Twitter users (SD = 437). ESPN’s SportsCenter followed the most users with 2,182, and Bomani Jones of Sirius Radio’s “The Evening Jones” followed 1,836 users. Conversely, ESPN’s Jay Bilas and Thayer Evans of Fox Sports followed no one on Twitter. Turning the tables, the journalists had large follower numbers with an average of 142,725 (SD = 438,663). New York Times had the most followers with 4,097,248, and ESPN had the second most with 2,233,233. The data revealed the journalists followed and received information from a large numbers of sources, including other journalists. They also had a combined 21.6 million followers, with the ESPN contingent having the largest following-10.8 million followers dispersed between its 48 journalists. Among the 151 journalists, 30 journalists stood out in terms of their relative prominence compared to their peers (Figure 4). The “Power 30” members were followed by an average of 28 other journalists within the network. Their media affi liation mimicked the larger network with their distribution among television (12–40%), blogs (6– 20%), newspapers (5– 17%), magazines (4–13%), newswires (2– 7%), and other (1– 3%). ESPN had the largest representation with five journalists, followed by CBS, Sports Illustrated, and Yahoo! Sports with four journalists each. These 30 journalists were followed by a total of 1.7 million Twitter users, and the most pop12


Fig. 4. Sociogram of the Power 30.

ular members included CNBC’s Darren Rovell (151,415 followers), Andy Katz of ESPN (128,445 followers), and Yahoo! Sports’ Pat Forde (121,861 followers). The Power 30 journalists assumed a central position within the network. Extracting them from the larger network, these journalists shared a combined 674 relationships amongst themselves and formed an extremely close-knit network with a density of .749. These journalists shared 75% of the possible relationships within their smaller group, and served as the network’s core, facilitating information sharing throughout the network. Discussion This study employed social network analysis to investigate the network formation and relationships among sports journalists as they discussed the Penn State football scandal via Twitter. This study holds several important implications which are now discussed. First, Twitter functions as a medium where sports journalists access their contemporaries to obtain important details about significant sports stories. In this case, the linkages exponentially increased from the 2nd–11th day of the story (43 connections to 2,777) before tapering off. For sports journalists, occupying a presence on Twitter is an essential task, particularly given Twitter’s capability to broadly disseminate breaking news instantaneously (Arceneaux & Schmitz-Weiss, 2010; Sanderson & Kassing, 2011). Twitter not only offers access to their peers, but also to sizeable audiences. Sports journalists who participate in these networks can increase the distribution of their commentary and stories through the work of other network members. In Hambrick & Sanderson: Gaining Primacy in the Digital Network

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other words, as a sports journalist shares pertinent information to the network, other journalists re-transmit this information to their followers, exposing the journalist to his/her audience, and signifying that this journalist is playing an integral role in the story. One need only look to Sara Gamin of The Patriot News who rose to national prominence during the Penn State story, an elevation that was in some part, attributable to other sports journalists encouraging their Twitter followers to access Gamin’s Twitter feed for the latest scoop on the Penn State story. This collegiality and promotion is interesting, as Gamin was essentially competition, and it seems doubtful that such advocacy would occur via traditional media platforms (e.g., newspaper columns), and it will be interesting to see if this trend continues in the future. As the Penn State story broke and gained national prominence, sports journalists flocked to Twitter to access the most current information which could then be distributed to their followers. Yet, after the story gained saturation, many network members moved on to other sports stories and exited the network, although it this may not equate with leaving Twitter. Indeed, sports journalists who did not rise to prominence with the Penn State story may be in search of the next sports story that will enable them to rise to the top of the Twitter hierarchy. For sports news consumers, Twitter exposes them to a variety of perspectives which they can customize to suit their preferences. This underscores the second implication from this study—that Twitter is a medium where vast quantities of information about sports news are available for consumption. As sports journalists gravitate to Twitter in an effort to remain visible to audiences, the data that flows from these exchanges and interactions pushes audiences to Twitter, rather than traditional news sites (e.g., newspaper, radio) as this information is available on-demand in convenient formats (e.g., via mobile devices). As this process unfolds, a symbiotic relationship is created as sports journalists must report on Twitter as sports fans expect news via Twitter, which comes about because of the large presence of sports journalists occupying Twitter. A third implication from the current study is the primacy that some sports journalists gained in the network, a fi nding that mirrors other research on news and social media sites (An et al., 2011; Kwak et al., 2010; Meraz, 2009). In this study, a small, influential group, the “Power 30” represented the informal network’s core, and played a heightened role within the network. These journalists had a combined 1.7 million followers and were followed on average by 28 other journalists within the top 151. As the most popular journalists, their central location within the network po14


sitioned them as key information hubs, receiving information from multiple sources and spreading that content to numerous followers, including other sports journalists. Rising to this position in the network places these sports journalists (and their affi liated employers) in prime agenda-setting positions. That is, sports journalists who rise in the network through their information, personality, or other factors, have exposure to hundreds of thousands, or even millions of potential audience members. As these journalists capture what might be termed a digital “market share,” they can capitalize by promoting work in other spheres. For example, “teasing” information via Twitter to re-direct followers to their column, or encouraging followers to “pay” for insider information (similar to ESPN). Interestingly, rising in the network is a function of one’s peers, and the promotion and integration present on Twitter, may result in some sports journalists reducing their individual relevance by promoting and encouraging their contemporaries. Accordingly, media outlets with multiple journalists using Twitter should capitalize on these processes. As their reporters gain primacy in the network, this hierarchy enables their sports journalists to sustain a “collective intelligence,” whereby popular media outlets create and share similar information continuously until consumers begin to read and interpret the information in this way (Kwak et al., 2010). This study demonstrates how sports journalists created informal social networks within Twitter to share and discuss noteworthy stories. The sports journalists assembled quickly to respond to and comment about the emerging Penn State news. As such, media outlets and sports journalists must recognize the role that social media plays in creating and transmitting news. Indeed, this arena cannot be ignored. The advent of social media has diversified sports media as athletes and sports figures routinely break news via their Twitter feeds or Facebook pages. Yet as this study suggests, sports journalists still have a place at the table, and collectively, provide a host of valuable information for sports consumers. Limitations and Future Research This study, as with all studies ahs several limitations which are now addressed. First, data were collected via snowball sampling and the majority of the sports journalists were male and represented major media outlets. Thus, the interactions may differ in a more diverse gender and media outlet sample. Second, the Penn State story represented one of many sport stories discussed during the two-week time period. Although the Penn State story was arguably the pre-eminent news story during this time peHambrick & Sanderson: Gaining Primacy in the Digital Network

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riod, an examination of other stories and informal networks may provide different results. Third, it would be important to examine multiple sports stories occurring simultaneously to ascertain if some journalists maintain primacy across stories, or if the hierarchy is more diverse. Another important direction to take in future work would be to examine the implications of network position as stories unfold. For example, how many Twitter followers does a sports journalist gain due to their position in the network? Similarly, if a sports journalist is being promoted by their peers, how many followers do these pronouncements induce? As with Sara Gamin, it may be that younger sports journalists rise to prominence as a result of their digital presence and collaboration much faster than they would without them. Conclusion Social media outlets such as Twitter are changing the sports media landscape. Indeed, when major sports stories unfold, social media domains are where the “action” happens and are where sports media consumers obtain the most current information. As this study suggests, some sports journalists are more central to a story than others, and those who occupy these integral network positions gain primacy and are key information disseminators as sports news emerges and unfolds. Thus, sports journalists must now utilize and compete with their peers in the social media as well as the traditional media circles. As social media continues to escalate up the sports media hierarchy, how sports journalists use and interact across these channels will be a vital research area. Twitter is no longer an optional resource for sports journalists; indeed, it is now an essential tool in their repertoire. Marion E. Hambrick (Ph.D University of Louisville) is an Assistant Professor in the Department of Health and Sport Sciences at University of Louisville. Jimmy Sanderson (Ph.D Arizona State University) is an Assistant Professor in the Department of Communication Studies at Clemson University.

Note 1. A “re-tweet” allows Twitter users to re-post another user’s post and add their own commentary to this message.

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