PDF (1524 KB) - Human Kinetics Journals

International Journal of Sport Communication, 2015, 8, 212 -232 http://dx.doi.org/10.1123/IJSC.2015-0011 © 2015 Human Kinetics, Inc.

Original Research

Why We Retweet: Factors Influencing Intentions to Share Sport News on Twitter Jan Boehmer University of Miami, USA

Edson C. Tandoc, Jr. Nanyang Technological University, Singapore The current study explored factors influencing content sharing on Twitter in the context of sport news. It employed a 2-step text-based analysis combining qualitative and quantitative approaches and found that 3 main categories of factors are influencing retweeting decisions: characteristics of the source, characteristics of the message, and characteristics of the user. A subsequent hierarchical-regression analysis revealed that factors related to a user’s encounter of a Tweet are the best predictor of retweeting intentions. More specifically, interest in the exact topic of the tweet, the perceived relevance that the tweet might have for the user’s own followers, and similarity in opinion play important roles. Implications for communication practitioners, as well as research investigating human behavior on social media, are discussed. Keywords: sharing, motivations, social media

The way individuals consume information about current events is changing. In addition to traditional news outlets, social media have become one of the main places to encounter news and have also enabled individuals to distribute and discuss the obtained information with their networks. In fact, some 50% of socialmedia users report sharing or reposting news stories while being online (Matsa & Mitchell, 2014). The microblogging site Twitter plays an important role in this process. While Facebook continues to be the largest social-media site in terms of number of active users, news sharing is not its primary use (Baek, Holton, Harp, & Yaschur, 2011). News consumption on Facebook remains incidental, with 78% of users reporting that they see news when browsing the site for other reasons (Matsa & Mitchell, 2014). Twitter, on the other hand, resembles several characteristics of more traditional news media and is frequently used to distribute and share news (Kwak, Lee, Park, & Moon, 2010).

Boehmer is with the Dept. of Journalism & Media Management, University of Miami, Miami, FL. Tandoc is with the Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore. Address author correspondence to Jan Boehmer at [email protected] 212

Why We Retweet: Sport News on Twitter 213

However, research addressing what specifically motivates individuals to share news on Twitter has only just begun. So far, studies have often focused on how news generally spreads through Twitter (Lerman & Ghosh, 2010), how Twitter can be used to curate information (Jackoway, Samet, & Sankaranarayanan, 2011), how it can be used to deliver personalized news (Abel, Gao, Houben, & Tao, 2011), and how important it is as a tool for users’ news consumption (Israel, 2009). On the other hand, studies focusing on sharing news through social media have often either taken a general approach utilizing a uses-and-gratification framework (e.g., boyd, Golder, & Lotan, 2010; Hanson & Haridakis, 2008; Lee & Ma, 2012) or analyzed retweeting behavior from a rather technical perspective (e.g., Suh, Hong, Pirolli, & Chi, 2010). However, retweeting behavior is a complex communication process (Liu, Liu, & Li, 2012) that needs to be addressed from diverse angles (Pilerot & Limberg, 2011), allowing insights into the multiple processes that might explain the likelihood of retweeting (Macskassy & Michelson, 2012). Overall, predicting retweetability is more complex than it seems, and the behavior is best predicted by a myriad of factors most likely operating at the same time. Therefore, the current study explored reasons for sharing information on Twitter going beyond traditional approaches. Employing a combination of qualitative and quantitative methods, a framework was established that describes how the characteristics of the source of a piece of information, the characteristics of the message itself, and the characteristics of the receiver simultaneously influence individuals’ intentions to retweet a message.

Review of Literature The fact that individuals have the tendency to share information, and to do so in a collaborative manner, has been widely recognized in fields such as library and information science (i.e., Savolainen, 2007, 2009; Talja & Hansen, 2006), organizational communication (i.e., Ardichvili, Page, & Wentling, 2003), and interpersonal communication (i.e., Cozby, 1972). With the proliferation of modern information technology, however, individuals have been enabled to exchange knowledge and information even more efficiently. Internet-based services have quickly become one of the most popular ways to communicate and share information in a variety of domains (Dimmick, Chen, & Li, 2004). Traditionally, information sharing can be understood as a set of activities by which information is provided by one entity to others, either proactively or on request (Berger & Luckmann, 1991). It is an active, mutual process in which two or more parties engage in both providing information and receiving information (Ardichvili et al., 2003; Van Den Hooff & De Ridder, 2004). Overall, information sharing is an important component in individuals’ construction of their world. It is an essential activity in shared social environments, as it helps bind groups and communities together (Davenport & Hall, 2002). Research has investigated potential reasons for individuals’ willingness to share particular pieces of information, ranging from academia (i.e., Pilerot & Limberg, 2011) to library and information science (i.e., Savolainen, 2009) and corporate environments (i.e., Ardichvili et al., 2003). Findings indicate that perceiving information as important to the public serves as the main motivation for sharing that piece of information (Ardichvili et al., 2003). Social-interaction ties, trust, the norm IJSC Vol. 8, No. 2, 2015

214 Boehmer and Tandoc

of reciprocity, identification, shared vision, and shared language, as well as expecting a positive outcome from the sharing, also influenced individuals’ attitudes and behaviors related to knowledge sharing (Chiu, Hsu, & Wang, 2006). Other reasons for sharing information in the mentioned contexts include the wish to connect on a personal level, to advance careers, and to campaign for projects (Dimicco et al., 2008), as well as trust, sense of community, and the congruence of the information with one’s own values (Sharratt & Usoro, 2003). On the other hand, greater selfinterest reduced support for sharing (Constant, Kiesler, & Sproull, 1994), while fear of criticism and that the information intended to be shared might not be correct or relevant to the audience were negative predictors (Ardichvili et al., 2003).

Information Sharing on Twitter Microblogging is providing a platform for quick and easy information sharing. It is a “form of blogging in which entries typically consists of short content such as phrases, quick comments, images, or links to videos” (Suh et al., 2010, p. 178). Twitter is an example of this type of communication. Individuals reposting or forwarding a message originally posted by another user on Twitter—usually through the process of retweeting—are engaging in a form of information sharing that resembles many of the characteristics mentioned in traditional information-sharing literature. While retweeting could simply be seen as the act of copying or rebroadcasting, “the practice contributes to a conversational ecology in which conversations are composed of a public interplay of voices that give rise to an emotional sense of shared conversational context” (boyd et al., 2010, p. 1). Based on these findings, we make the case that retweeting can be looked at using an information-sharing perspective to investigate multiple angles of potential reasons for which individuals share information on Twitter. The current study does so in the context of sport news. Sport is the third most frequently seen topic on social media, behind entertainment and information about events in users’ local communities (Matsa & Mitchell, 2014). Athletes, professional communicators, journalists, and fans frequently use the microblogging platform to gather and distribute information (Sheffer & Schultz, 2010). Twitter is even more popular than Facebook in the context of sport news (Reed, 2013). However, specific research on why individuals share sport content on this platform is rare. Studies at the intersection of sport and social media have mainly investigated potential motivations for following athletes on Twitter (e.g., Clavio & Kian, 2010; Frederick, Lim, Clavio, & Walsh, 2012; Witkemper, Lim, & Waldburger, 2012), how athletes and organizations use Twitter (e.g., Browning & Sanderson, 2012; Clavio, Walsh, & Vooris, 2013; Hambrick, Simmons, Greenhalgh, & Greenwell, 2010; Pegoraro, 2010; Sanderson, 2011), how Twitter is changing sport journalism (e.g., Sheffer & Schultz, 2010) and media culture overall (e.g., Hutchins, 2011), how Twitter is used as a marketing tool in the realm of sport (e.g., Hambrick & Mahoney, 2011), and how it facilitates the development of parasocial relationships between athletes and their fans (e.g., Frederick et al., 2012; Kassing & Sanderson, 2010). Given the prevalence of sport as a topic on social media, the prominent use of Twitter by sport communicators and their audiences, and the lack of research studying information sharing at this intersection, represents a valuable opportunity

IJSC Vol. 8, No. 2, 2015


to not only investigate this important subsection of social-media use in a popular context but also contribute to understanding human behavior on social media in more general terms.

Reasons for Information Sharing on Twitter Generally, the need to share information (Baek et al., 2011); status seeking, sociability, and informativeness (Ma, Lee, & Goh, 2011); social considerations (Sousa, Sarmento, & Mendes Rodrigues, 2010); and prior experience with the medium (Lee & Ma, 2012) have been pointed out as reasons for sharing news-related content on social media. More recently, a growing number of scholars have specifically focused on understanding potential factors that lead people to retweet certain messages. The general argument in this line of research is that “multiple processes are participating” in explaining the likelihood of retweeting (Macskassy & Michelson, 2012, p. 219). A review of the growing literature on predicting retweetability shows that factors affecting retweet behavior can be placed into three major categories that are related to features concerning either the content or the context of tweets (Suh et al., 2010). These categories include the characteristics of the source of the message (Suh et al., 2010; Wang, Liu, Zhang, & Li, 2013), the characteristics of the message itself (Macskassy & Michelson, 2012), and the characteristics of the user confronted with the message (boyd et al., 2010; Rudat, Buder, & Hesse, 2014) However, most of the existing studies investigating factors influencing retweetability are based on the analysis of publicly available tweets, and while they illuminate patterns that emerge from an examination of actually retweeted messages, they do not comprehensively reflect the perspective of the users who decided to retweet. At best, the findings of these tweet-based studies provide us a starting point to explore retweeting intention from the perspective of actual users engaged in the behavior—an approach that this current study undertakes. We do so by first explicating the three main categories and then investigating them in detail. Source Characteristics. An experiment designed to test a model to predict retweetability found that a combination of heuristic and systematic cues exerted significant effects (Liu et al., 2012). The study found that, “for heuristic cues, source trustworthiness, source expertise, and source attractiveness all have significant positive effects on retweeting” (p. 459). The number of followers and followees of a source, as well as the age of the account, also positively affected retweetability, while number of past tweets did not matter (Suh et al., 2010). Another study concluded that the features of a tweet’s creator have more influence than the feature of the tweet itself in predicting retweetability (Wang et al., 2013). The credibility of the source has also been related to the likelihood of a tweet’s being shared. Castillo, Mendoza, and Poblete (2011) found that popular tweets were generally related to credible news. Taken together, the current study concludes that source characteristics play an important role in individuals’ reasons for sharing news on Twitter. However, since there exists no complete account of potential source-related factors affecting retweeting intention in the context of sport news, we ask the following:

RQ1: What specific source-related factors influence a user’s intention to retweet?

IJSC Vol. 8, No. 2, 2015


Message Characteristics. Several characteristics of the message itself also influence the likelihood of its being retweeted (Nagarajan, Purohit, & Sheth, 2010). A large data analysis of tweets found a number of specific content-related variables that increase the likelihood of a message’s being retweeted. An analysis of 74 million tweets found that tweets with hashtags tend to be retweeted more often (Suh et al., 2010), while another study found that the number of multimedia in information also exerted positive effects (Liu et al., 2012). However, there is disagreement on the impact of links, with one study finding that tweets including URLs tend to be retweeted more often (Suh et al., 2010), while another study concluded that the number of URL links included in a tweet had a negative impact on retweetability (Liu et al., 2012). A point of consensus, however, is that contextual characteristics play an important role in individuals’ reasons for sharing news on Twitter. Testing this in a sport-news context, we ask the following:

RQ2: What specific content-related factors influence a user’s intention to retweet? User Characteristics. A simple question a researcher posed on Twitter about

why people retweet yielded a number of responses referring to various internally motivated reasons, including “to amplify or spread tweets to new audiences”; “to entertain or inform a specific audience, or as an act of curation”; “to publicly agree with someone”; and “to validate others’ thoughts,” among others (boyd et al., 2010, p. 6). In these responses, the importance of a user’s orientation to his or her followers—his or her audience—emerges. Indeed, an experiment concluded that when users are aware of their audiences’ preferences, they adapt their communication behavior accordingly (Rudat et al., 2014). This is consistent with research finding that retweets have a strong social component (Yang & Counts, 2010) and are often used to make statements positioning the retweeter in a social context (Cha, 2010). Aside from this orientation to followers, attributes of the retweeter, such as gender and life satisfaction, have also been shown to affect social-media use for news-related purposes (Glynn, Huge, & Hoffman, 2012). However, this stream of research, focusing on user-related factors in retweeting behavior, remains underdeveloped, especially when compared with the two other groups of factors. This is largely due to method-related reasons, as an analysis of retweeted tweets alone cannot reveal the motivation of the retweeter. In using a combination of qualitative and quantitative methods in an experimental setting, the current study contributes to this specific research area by expanding the focus on the impact of user-related factors in retweeting behavior. Thus, we ask the following: RQ3: What specific user-related factors influence a user’s intention to retweet?

An Interplay of Factors. In summary, three main groups of factors predicting

retweeting intention emerge from the literature. These are characteristics of the source of the message (Suh et al., 2010; Wang et al., 2013), characteristics of the message itself (Macskassy & Michelson, 2012), and characteristics of the user confronted with the message (boyd et al., 2010; Rudat et al., 2014). However, while most researchers suggest that these factors interplay, making retweeting behavior a complex and intriguing communication process (Liu et al., 2012; Suh et al., 2010), few studies have actually attempted to investigate how prevalent the IJSC Vol. 8, No. 2, 2015


individual factors are within users and how much they contribute to the process of making a decision to retweet. One study concluded that content-based retweeting models were better than most other models at accounting for retweet behaviors (Macskassy & Michelson, 2012), but what specific factors related to the content of a tweet drive retweets remains unexplored. To investigate this further in the context of sport news, we finally ask the following question: RQ4: How do these source-, content-, and user-related factors compare in predicting retweeting intention?

Methodology Sample and Procedure The current study is part of a larger experiment investigating several communication processes on Twitter and was conducted in early 2014. Participants were recruited from undergraduate and graduate courses at a large Midwestern university in the United States. The university registrar’s office sent out an invitation to a random sample of 10,000 students who had the chance of being entered into a raffle of two 50-dollar Amazon gift certificates. At least 1,005 participants completed the overall experiment, resulting in a response rate of 10.05%. From this pool of participants, about 70% reported having an active Twitter account that they used at least once a week. A subset of those participants was selected to review a set of three tweets attributed to sport-media personality Bill Simmons that were created to represent a cross-section of Simmons’s actual tweets. Tweets included Simmons’s evaluation of a player signing with a team in the NBA, commenting on a player’s latest statement, and reporting news (see Figure 1). Simmons had emerged as the best-known sport-media personality in a pretest, during which university students (n = 53) were presented with the 14 most followed sport-media personalities on Twitter at the time of the experiment (Simmons, Adam Shefter, Peter King, Skip Bayless, Mike Wilbon, J.A. Adande, Jay Glazer, Chris Broussard, Chris Mortensen, Jay Bilas, Mike Tirico, John Clayton, Adrian Wojnarowski, and Mel Kiper, Jr.). Participants then had to identify each sport-media personality from a set of three images, indicate whether they were following him or her on Twitter, and indicate whether they had watched/read his or her work. Only participants who viewed the stimulus material, knew Bill Simmons, and responded to all survey items were retained in the sample for the current study, resulting in a final sample size of 329 participants. Familiarity with Simmons was required so that participants could make more realistic judgments of whether they would retweet the encountered content. The final sample used for the current study was comparable to the demographic profile of the university’s student population and relatively close to the Twitter demographic in the available categories. Both sample and student populations were predominantly White (sample = 78%, population = 79%, Twitter = 71%), followed by Asian (sample = 10%, population = 11%) and African American (sample = 6%, population = 7%, Twitter = 9%). The majority of participants (39.6%) had been using Twitter for 1–2 years, were accessing it more than three times per day (37.5%), and had 100–200 followers. Table 1 provides an overview of the comparison in key demographic variables. IJSC Vol. 8, No. 2, 2015


Figure 1 — Example of stimulus material.

Table 1 Comparison of Study Sample With University Student Population and Twitter Variable Age Gender (male/female) Race White African American Asian Hispanic other

Sample

Twitter

University

20.9 44/56

n/p 50.8/49.2

20.4 47/53

78% 6% 10% 2% 6%

71% 9% n/p n/p n/p

79% 7% 11% 3% 3%

Note. n/p = not provided by Twitter.

Measures Retweeting Intention. To elicit factors that influence retweeting behavior, partici-

pants answered the following open-ended question after exposure to the stimulus material: “Please explain briefly why you would or would not share/recommend/ retweet the posts you just reviewed.” In addition, participants were asked whether they agreed or disagreed with the statement that the tweets they just reviewed were worth sharing with others and whether they would retweet the tweets they just reviewed, r(329) = .614, p < .001. Both responses were measured on a 5-point IJSC Vol. 8, No. 2, 2015


Likert scale from strongly disagree (1) to strongly agree (5) that was adapted from previous research (Alhabash et al., 2013). Control Variables. To control for the assumption that retweeting likely depends on situational and topical factors (Wang et al., 2013), participants also responded to a 6-item instrument assessing their interest in sport on a 7-point Likert-type scale that included questions such as “I am interested in sport” and “I stay up to date with current developments in sport.” The scale yielded a Cronbach’s alpha of .94. Furthermore, participants reported the degree of their familiarity with Simmons by indicating whether they had encountered him or his content in one or more of the following contexts: Web site, television, Twitter, Facebook, or live event. Positive responses were coded as 1 and added up to form a familiarity index ranging from 0 to 5. Participants also indicated their age and gender and whether they already followed Simmons on Twitter. Finally, to control for the assumption that information sharing through technology potentially depends on individuals’ familiarity and usage patterns of that technology (Van Den Hooff & De Ridder, 2004), participants indicated how long they had been using Twitter, how frequently they were using it, and how many followers they had. Table 2 provides an overview of the descriptive statistics of the main variables used in the current study.

Data Analyses To answer the questions posed herein, the current study employed a two-step text-based analysis that combines qualitative and quantitative content-analysis approaches and relates the results to participants’ survey responses using hierarchical multiple regression. Qualitative Analysis. In the first step, the qualitative analysis took a constantcomparative approach that allowed patterns to emerge from the data (Tracy, 2012). The first stage was the open-coding phase, where one of the researchers read all responses for a preliminary “soak” in the data before proceeding with line-by-line coding (Saldana, 2012). Consistent with the constant-comparative approach (Glaser, 1965; Glaser & Strauss, 1967), each response was compared with the preceding response as codes began to emerge. Axial coding, or when the codes start to link together under unifying conceptual bins, followed this step (Lindlof & Taylor,

Table 2 Descriptive Statistics of Key Variables Variable

Mean

SD

alpha

Intention to retweet Sport interest Familiarity with Simmons Length of Twitter use Frequency of Twitter use Number of followers

2.48 4.09 0.58 2.09 2.98 2.81

.93 1.67 1.08 0.84 2.14 1.42

.78 .94 — — — —

IJSC Vol. 8, No. 2, 2015


2010). This is when the related codes are combined to form categories. Once the categories were fully developed, narratives were constructed around them to distinguish them clearly from one another. Quantitative Analyses. The reasons for sharing that emerged from the qualitative analysis were used to code the entire data set using quantitative content analysis (Riffe, Lacy, & Fico, 2014). In this process, a participant’s response served as the unit of analysis and could include multiple reasons for sharing. In addition, participants could mention the reasons in a positive or negative way. For example, participants reported that the subject of the tweet was either interesting to them (positive) or that they had no interest in the topic (negative). Positive responses were coded as 1; negative responses were assigned –1. If a category was absent, it was coded 0. To determine the reliability of the coding manual, two coders familiarized themselves with the definitions of the categories that emerged from the qualitative coding and coded 10% of the data. Krippendorff’s alpha, Cohen’s kappa, and Scott’s pi were calculated for all coded variables in the data set (Riffe et al., 2014). Reliability for the variables ranged from .75 to 1.0 (see Table 2) and therefore met the standards of acceptable reliability laid out by previous research (Riffe et al., 2014). After reliability was established, the two coders divided the remainder of the responses and coded the data set individually. Finally, a five-stage hierarchical regression was computed with retweet intentions as the dependent variable. This was done to compare the impact of the individual factors and the overall categories on how much they add to the prediction of intentions to retweet based on a theoretically derived model (Cohen, 2001). Demographic variables (age, gender), sport interest, and familiarity with Simmons were entered at Stage 1. Twitter-use variables (length of Twitter use, frequency of Twitter use, number of followers) were entered in Stage 2 to control for these factors. In Stage 3, the characteristics related to the source of the tweet were entered, as they have been found to have less explanatory power than the factors representing the content-related characteristics, which were entered at Stage 4. Finally, Stage 5 was composed of the factors representing the user-characteristics construct derived from the qualitative coding, which had not been tested in this context before this study.

Results The categories that emerged from the data fell into the three main concepts that were consistent with what was established in the literature: source characteristics, message characteristics, and user characteristics. Through the constant-comparative approach, specific factors related to each category were also classified, providing a more nuanced and detailed description.

Source Characteristics Credibility (4% of Sample). Some users said they would retweet because Simmons appeared to be a reliable or trustworthy source. Participants based their judgments on familiarity with the source, from the tweets that they encountered during the experiment, and from other heuristics such as number of followers. One participant said that Simmons “seems like a knowledgeable source for NBA

IJSC Vol. 8, No. 2, 2015


news.” Others, however, chose not to retweet because they doubted his credibility; for example, “I don’t follow Bill Simmons, so I am not completely familiar with his credibility, and I don’t know him well enough to know if he is tweeting good information.” Prominence (4.9%). Some users said they would retweet because the source

is famous. Others chose not to retweet because they did not know Simmons; for example, “I don’t know how popular he really is and therefore have no opinion on his tweets.” This response shows the important role of familiarity with a source in decisions to share information on social media.

Attitude Toward the Source (6.7%). A source might appear knowledgeable and popular, but particular people might still not like him for other reasons. Interest in the source of information is therefore also important in users’ decision to share a post or not. For example, one participant simply said, “He is an idiot.” This might have been inferred from the tweets associated with Simmons in the experiment, but in this response, it was the participant’s attitude toward the information source that influenced his decision.

Message Characteristics Informativeness (8.8% of Sample). Informativeness refers to the amount of factual information in a tweet. Some users said they were likely to retweet a message that is informative, reporting facts instead of opinion; for example, “I would share these with people who follow sport, because his tweets seem primarily factual and less opinionated.” The same message characteristic turned off some participants, however, who said they were less likely to retweet a tweet that contained only facts, since these are details they can also learn from other sources such as sport Web sites: “Most of the tweets are just informative information released through news feeds and sport lines.” Originality (5.8%). Originality refers to how novel or original a tweet is. Some users look for originality of posts in deciding whether to retweet. Posts that merely repeat what other sources have disseminated are not considered retweetable. One participant said, “It’s basic sport information that anyone who would want to know would know already.” Fun (3.7%). A few users said they would retweet the tweets they find funny, indicating that humor is one of the considerations that affect decisions to share on social media. Thus, some of the participants who did not find the displayed tweets humorous said they would not retweet: “I would not share the posts ’cause the tweets are not funny.” Bias (9.4%). The presence of the source’s opinions in the tweets got mixed

responses. Some participants valued the presence of opinion; for example, “I think that I would share Bill’s tweets because the tweets show his desire and will.” Some participants said they would not retweet messages that contained too much opinion. Others dismissed these tweets as merely an expression of the source’s personal views and therefore something they would not share. For example, one participant said, “They seem biased and useless.” IJSC Vol. 8, No. 2, 2015


Style (2.1%). Some participants referred to the style of the tweets. A few users

said they would retweet messages that are well written. Others found the tweets to be dry. A participant said she would not retweet because “they are frivolous, not well written, and mostly tacky.”

User Characteristics Interest (52.6% of Sample). Interest refers not only to a user’s level of interest in sport in general but also to the interest in the specific sport that was mentioned in the tweets (professional basketball). Users said they would share the tweet because they are fans of the particular sport. One participant said, “As a fan of the NBA, I would share his views with my friends.” However, other participants said they were not interested in the particular sport, and because of this they were not likely to retweet. One participant who indicated that he or she was not going to retweet explained, “I don’t care about basketball.” Relevance (14.6%). Others were interested in the sport mentioned, but not in

the specific teams or players the tweets referred to. Therefore, these users said the tweets were not relevant to them, even if they were interested in sport and the NBA. One of the participants said, “While I’m a sport fan, I’m not interested in the teams or athletes that he tweeted about. If they were about a team or players that I rooted for, then my answers would have been different.” Thus, while this participant is generally interested in sport, he did not perceive the tweets as relevant to his interests, because they were about teams and players he was not following. The responses showed that users differentiated between what was interesting and what was relevant.

Similarity in Opinion (7.3%). Similarity in opinion refers to the degree to which users perceive a tweet as containing an opinion similar to their own. Some participants said they would only retweet content that is consistent with their personal beliefs. One explained, “I would retweet Simmons’ tweets because I agree with the things he said.” The same standard, however, was invoked by other participants who explained their reasons for not intending to retweet: “None of the sport information just said was anything I necessarily agreed with.” Impact on Others (8.2%). Many of the participants articulated how much they considered their own followers in their decision to share, which is very similar to how traditional news media take into account their target audience. Some participants said they would share a tweet if they thought it would appeal to their Twitter followers. One said,

I would share some of Bill Simmons’ tweets regarding sport with some of my friends here at [the university] because most of my friends are really into all the sport and events here on campus, and they would appreciate the information sent out via twitter by Bill Simmons. Habit (8.2%). Some users said they just do not retweet in general. Others said

they only retweet friends, while others said they do not retweet celebrities. A few said they are just observers on Twitter. Thus, they are not likely to share any tweets. One participant said, “I really do not retweet anything that often so that’s why I wouldn’t republish them.” IJSC Vol. 8, No. 2, 2015


Overall, the qualitative analysis of participants’ responses revealed a subset of factors influencing retweeting decisions that could be classified into the three categories that emerged from previous literature on information sharing on Twitter. However, the results also expand previous research by adding subdimensions to the established categories.

Results of Quantitative Analyses A quantitative content analysis based on the categories that emerged from the qualitative component found that personal interest was the most frequently mentioned category. Some 50% (n = 173) of participants indicated that interest in the subject of the tweet would influence their decision on whether or not to retweet a particular tweet (see Table 3). Some 14% (n = 48) mentioned relevance, while bias (9.4%) was the third most frequently mentioned factor, followed by the informativeness of the tweet (8.8%), the perceived impact on followers, and habit, with each about 8%. Taken together, the factors related to the characteristics of the user (interest, relevance, similarity in opinion, impact, habit) were mentioned most frequently, followed by the characteristics of the message (informativeness, originality, fun, bias, style) and then finally the characteristics of the source itself (credibility, interest in the source, prominence).

Table 3 Individual Retweet Factors (by Category), Distribution, and Reliability Factor

% in sample

n (pos/neg)

Scott’s pi

Krippendorff’s alpha

Cohen’s kappa

52.6 14.6 7.3 8.2 8.2

173 (27/146) 48 (8/40) 24 (10/14) 27 (17/10) 27 (1/26)

.902 .828 .749 .949 .749

.903 .829 .751 .950 .751

.902 .828 .749 .949 .749

8.8 5.8 3.7 9.4 2.1

29 (21/8) 19 (5/14) 12 (4/8) 31 (29/3) 7 (2/5)

.821 .851 .794 .916 1

.823 .852 .795 .917 1

.822 .851 .794 .916 1

6.7 4.0 4.9

22 (2/20) 13 (7/6) 16 (5/11)

.885 .882 1

.886 .883 1

.886 .883 1

User interest relevance similarity in opinion impact habit Message informativeness originality fun bias style Source attitude toward source credibility prominence Note. Pos = positive; neg = negative.

IJSC Vol. 8, No. 2, 2015


A hierarchical multiple regression was conducted to answer RQ4. The first block contained demographic variables for control. Gender (β = –.24, p < .001) and familiarity with Bill Simmons (β = .12, p < .05) contributed significantly to the regression model, F(4, 224) = 7.80, p < .001. Overall, the model accounted for 7.7% of the variation in retweeting intentions (see Table 4). Introducing Twitteruse variables (length of use, frequency of use, number of followers) in the second stage only explained an additional 2% of variation, F(4, 221) = 5.53, p < .001. This

Table 4 Results of Hierarchical-Regression Models Predicting Intention to Retweet Stage 1: Demographics age gender interest in sport familiarity with Simmons Stage 2: Twitter use length of Twitter use frequency of Twitter use number of followers Stage 3: Source characteristics attitude toward source credibility prominence Stage 4: Content characteristics informativeness originality fun bias style Stage 5: User characteristics interest relevance similarity in opinion impact habit Incremental R2 (%) Total R2 (%)

Stage 1

Stage 2

Stage 3

Stage 4

Stage 5

–.08 –.24*** .04 .12*

–.10 –.24*** .07 .14

–.07 –.21*** .05 .08

–.06 –.21*** .05 .10

–.06 –.10* .01 .03

.11* .13* .02

.11 .12 .03

.11* .12* .05

.08 .07 .05

.14** .17** .05

.13* .13* .02

.17*** .12** .03

.28*** .19*** .07 –.06 .133**

.19*** .19*** .08 –.06 .13**

14.2*** 27.8***

.37*** .19*** .16*** .10* .05 19.0*** 46.8***

7.70***

1.1 8.80

Note. Entries are standardized regression coefficients. N = 329. *p < .05. **p < .01. ***p < .001. IJSC Vol. 8, No. 2, 2015

4.8*** 13.6***


change in R2 was not significant. However, length of Twitter use (β = .11, p < .05) and frequency of Twitter use (β = .13, p < .05) emerged as significant predictors. In the third stage, variables describing the characteristics of the source (credibility, interest in the source, prominence) were entered. This stage explained an additional 5.5% of the variance in retweeting intention, F(10, 318) = 6.18, p < .001. More specifically, interest in Bill Simmons (β = .14, p < .01) and perceptions about his credibility (β = .17, p < .01) emerged as significant predictors. The fourth stage included factors related to the characteristics of the message (informativeness, originality, fun, bias, style) and explained an additional 14.9% of variance in retweeting intention, F(15, 313) = 9.43, p < .001. Informativeness (β = .28, p < .000), originality (β = .19, p < .001), and style (β = .13, p < .01) were significant predictors. Finally, adding the characteristics of the user (interest, relevance, similarity in opinion, impact, habit) in the final stage explained an additional 18.9% of the variance in retweeting intention, F(20, 308) = 15.42, p < .001. Interest (β = .37, p < .001), relevance (β = .19, p < .001), similarity in opinion (β = .16, p < .001), and impact on others (β = .10, p < .05) were significant predictors. Table 4 shows an overview of the hierarchical-regression results. Overall, the most important set of predictors were the characteristics of the user, followed by the characteristics of the content. Looking at the individual factors included in the final model, interest in the specific topic of a tweet emerged as the most important predictor of retweet intention, explaining almost 11% of the variance alone. Relevance, informativeness, originality, and participant attitude toward the source (each with 3%) were also important predictors in the final model. Overall, all variables included in the final model explained 46.8% of the variance.

Discussion Through a combination of qualitative and quantitative analyses, this study documented different reasons that influence a Twitter user’s intentions to retweet a message in the context of sport news and also compared the extent of influence each factor exerts. Overall, the current study found that the three categories derived from previous literature (characteristics of the user, the message, and the source) were also present in the context of sharing sport news on Twitter. Looking more closely at the individual factors constituting those categories, this study found that one’s level of interest in a tweet’s exact topic, the perceived relevance of the tweet, how similar the information contained in a tweet is to the user’s personal opinion, and one’s perception of how a tweet would affect one’s followers are the user characteristics that affect intentions to retweet. In terms of content-related characteristics, a tweet’s style, informativeness, and originality affect retweetability. In terms of the characteristics of the source, perceptions of credibility and likeability exert influences. Finally, user-related factors exerted the strongest influence, with personal interest demonstrating the strongest effect among the variables tested. The findings of this study depart to a certain degree from earlier studies in this area. Some previous studies concluded that content-related characteristics are the most influential factors predicting retweetability (Macskassy & Michelson, 2012). While the current study indeed found that content-related factors exerted significant effects, more factors related to the characteristics of Twitter users facing the retweet IJSC Vol. 8, No. 2, 2015


decision best predicted retweeting intention. This finding can largely be explained by the methodological approach undertaken in this study. Analysis of publicly available tweets, as conducted in many previous studies, has illuminated a myriad of factors affecting retweetability, but studies that used this approach were limited by what they could find in the content itself. By approaching the issue of retweeting from a different perspective, using an experiment and asking users to describe their reasons for retweeting or nor retweeting, the current study provides a complementary approach to allow a more complete and multiperspective understanding of what predicts information sharing on Twitter. In allowing users’ reasons to emerge from the data, instead of appropriating existing typologies of social-media-use motivations, this study also took into consideration the importance of contextual factors in understanding people’s reasons for sharing information. This multianalytical approach also provided a more detailed understanding of the data, allowing a more nuanced description of the various factors affecting retweeting intentions. Looking more specifically at some of the results of the current study, one of the most interesting variables is personal interest, which was the strongest predictor of retweeting intentions. This is likely to be facilitated by the characteristic of Twitter as a customizable platform. Users select which other users to follow mainly based on their interests and are therefore more likely to encounter tweets that deal with topics they are generally interested in. However, the current study found that it is not necessarily general interest in a topic that drives retweeting but that decisions to share a tweet evoke a much more fine-grained analysis of whether or not a topic is interesting. Despite controlling for participants’ general interest in sport, many participants indicated that they were not interested in the specific topic of the tweet. Even participants already following Bill Simmons, who arguably have a general interest in the topics that he tweets about, were not automatically more likely to share his tweets. This indicates that individuals are very selective with their decision to retweet, even within the range of Twitter users they already follow. This has broad practical implications for sport-media personalities, as it emphasizes the importance of communicators’ knowing their audiences’ interests when addressing them with the intention to have their information shared on Twitter. More in-depth knowledge seems to be needed. For example, while individuals following a journalist covering the NBA are likely to be interested in basketball, they might not be willing to retweet general information. Rather, the results of the current study suggest that individuals are looking for specific information that not only meets their specific interests but also provides original content from a credible source. This supports the idea of Twitter’s providing a venue for niche markets. Instead of reporting on general issues, communicators need to analyze how their followers perceive them in terms of expertise and interests and how they can position themselves in this market. It should be the communicators’ goal to provide unique content in a specialized area that lines up with their audiences’ interests. This is particularly relevant to communicators covering a specific beat, as they have the opportunity to focus on a clearly defined area of expertise. It is therefore understandable that communicators work on making their messages interesting and appealing on different levels, such as creating novel content using a recognizable style. Data showed that informativeness and originality were both positive predictors of retweeting intention. Style was also a significant predictor, but with a slightly weaker magnitude. In an age of information overload, it appears that participants IJSC Vol. 8, No. 2, 2015


in the experiment value novelty. For a message to stand out and not be drowned by a deluge of information, it has to be original. Good information itself is also considered important, which is consistent with previous findings (Lee & Ma, 2012). Finally, users’ perceptions about the source of the tweet also matter. Two specific variables—attitude toward the source and credibility—were found to predict retweeting intention. In the qualitative analysis, some users mentioned heuristics such as number of followers that affected their perception of the source’s credibility. However, prominence was not a significant predictor, which also showed that many users tend to go beyond obvious signals of popularity when forming their attitudes toward a tweet’s source. Instead, they also make judgments based on the text of the tweet and information available in the source’s profile. The problem, however, is that in the end the arbiter of all those judgments remains the actual user. For example, in the qualitative analysis, some users described the tweets as funny and the source as appearing to be credible but still decided not to retweet because they were simply not interested in the specific topic of the tweet. In addition, often it is not easy to assess whether users regard a particular piece of information as interesting or relevant at any given time, and it is not only a Twitter user’s own interest that matters. Even if a user might be interested, a Tweet might not be retweeted given the finding that individuals also strongly consider the interests of their own followers when making the decision to retweet or not. This confirms boyd et al.’s (2010) findings that retweeting is associated with the motivation to entertain a specific audience.

Limitations and Future Research For many other experiments, a sample of college students experienced with working with a computer in an online environment would limit the results’ generalizability. However, the goal of the current study is not to produce results that apply to populations not familiar with using computers and the Internet, such as seniors and low-income and low-education demographics, as they are unlikely to be affected by the studied principles in the first place. Choosing a student population serves a purpose, as this demographic is more likely to encounter information on Twitter and is therefore more likely to be affected by it. The sampled population, while slightly different from the demographics of all Twitter users, can be considered as being at the forefront of a new technological revolution (Gil de Zúñiga, Veenstra, Vraga, & Shah, 2010). The current study, which was part of a larger online experiment, also relied on artificially created and selected tweets to ensure complete control over the experiment and provide a variety of characteristics usually found in Bill Simmons’s tweets. In the real world, however, Twitter is not a controlled environment. As mentioned herein, users constantly adjust whom they follow and therefore prefilter which tweets they encounter. Therefore, as a next step, developing scales and testing the specific categories found in the current study with followers of a specific account presents a viable area of investigating trends in retweeting. Furthermore, the variable of bias—or the presence of opinion in a tweet—which did not exert significant effects, merits further investigation. The qualitative analysis showed that while some users shunned opinionated tweets, others considered subjectivity in a tweet a reason to share that message for containing an added value, IJSC Vol. 8, No. 2, 2015


something a Twitter source “owns” instead of merely repeating what is available from other sources. The pattern that emerged from the qualitative analysis can explain the lack of any statistically significant effects of bias on retweeting intention based on the quantitative analysis. For some people, presence of bias increases retweeting intention. For others, it does not. It is therefore plausible that other factors moderate the influence of bias on retweetability. Especially in the realm of sport, where communicators frequently offer personal information and opinions (Roberts, 2013; Sanderson & Hambrick, 2012), it is plausible that individuals following a specific communicator on Twitter are in line with that communicator’s beliefs—even if they are biased. This is well reflected in the current data, which highlight similarity in opinion as an important factor in individuals’ intentions to retweet. Choosing Bill Simmons as the originator of the tweets used in the current study certainly contributed to the profound results in this regard. Simmons is known to be polarizing and likely to elicit more intense reactions than sport-media personalities who are considered neutral. This highlights an important limitation of the current study, as a portion of the results (factors such as bias, being in line with personal opinion) is more likely to apply to opinionated content than it is to straightforward news reporting. It would be interesting to explore in the future how Twitter users perceive neutral news entities or Twitter users with whom they have personal (and even offline) relations and interactions and whether that perception affects retweeting intention. In addition, participants in the current study only viewed a small subset of tweets. It must be assumed that retweeting behavior forms over a longer period of time and that some of the specific intentions mentioned by participants are a product of the tweets they reviewed. However, to ensure validity of the current study, participants entering the online experiment were required to be familiar with Simmons and follow him on Twitter. Therefore, it is likely that they based their retweeting decisions not only on the presented tweets but on a combination of the stimulus material and the larger corpus of tweets they had been exposed to in the past.

Conclusion In conclusion, this current study complements previous research that investigated retweeting behavior and found that it is a complex phenomenon, for it is a behavior affected by a gamut of variables that coexist and most likely also interact with one another. Three main categories of factors were found (characteristics of the user, the content, and the source), with a user’s personal interest in the specific topic of a tweet emerging as the most important predictor of retweeting. These findings not only offer further insights into the complex determinants of human behavior on social media but also provide valuable insights for professional communicators using Twitter as a tool to distribute their content. In a period when traditional media organizations are increasingly turning to social media to win back audiences they have been losing by increasing interactivity, providing a quick feedback loop, and even developing a relationship with followers, comprehending the complex phenomenon of information sharing online is paramount. In approaching this research area from the perspective of Twitter users and using a multianalytical approach, this study hopes to contribute to this much-needed understanding. Even though this study was conducted in the context of sport news, IJSC Vol. 8, No. 2, 2015


the important role of personal interest in predicting information-sharing behavior and the relationship between other variables is also plausible across other contexts.

References Abel, F., Gao, Q., Houben, G-J., & Tao, K. (2011). Analyzing user modeling on Twitter for personalized news recommendations. In J.A. Konstan, R. Conejo, J.L. Marzo, & N. Oliver (Eds.), User modeling, adaption and personalization (Vol. 6787, pp. 1–12). Berlin: Springer. Alhabash, S., McAlister, A.R., Hagerstrom, A., Quilliam, E.T., Rifon, N.J., & Richards, J.I. (2013). Between likes and shares: Effects of emotional appeal and virality on the persuasiveness of anticyberbullying messages on Facebook. Cyberpsychology, Behavior, and Social Networking, 16(3), 175–182. doi:10.1089/cyber.2012.0265 Ardichvili, A., Page, V., & Wentling, T. (2003). Motivation and barriers to participation in virtual knowledge-sharing communities of practice. Journal of Knowledge Management, 7(1), 64–77. doi:10.1108/13673270310463626 Baek, K., Holton, A., Harp, D., & Yaschur, C. (2011). The links that bind: Uncovering novel motivations for linking on Facebook. Computers in Human Behavior, 27(6), 2243–2248. doi:10.1016/j.chb.2011.07.003 Berger, P.L., & Luckmann, T. (1991). The social construction of reality: A treatise in the sociology of knowledge. London, UK: Penguin. boyd, d.m., Golder, S., & Lotan, G. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter. In Proceedings of the 43rd Hawaii International Conference on System Sciences (HICSS) (pp. 1–10). Honolulu, HI: IEEE. Browning, B., & Sanderson, J. (2012). The positives and negatives of Twitter: Exploring how student-athletes use Twitter and respond to critical tweets. International Journal of Sport Communication, 5(4), 503–521. Castillo, C., Mendoza, M., & Poblete, B. (2011). Information credibility on Twitter. In Proceedings of the 20th International Conference on World Wide Web (pp. 675–684). New York, NY: ACM. Cha, E. (2010). The Twitter ties that retweet: Information diffusion in social movements. Stanford, CA: Department of Communication, Stanford University. Chiu, C-M., Hsu, M-H., & Wang, E.T.G. (2006). Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems, 42(3), 1872–1888. doi:10.1016/j.dss.2006.04.001 Clavio, G., & Kian, T.M. (2010). Uses and gratifications of a retired female athlete’s Twitter followers. International Journal of Sport Communication, 3(4), 485–500. Clavio, G., Walsh, P., & Vooris, R. (2013). The utilization of Twitter by drivers in a major racing series. International Journal of Motorsport Management, 2(1), Article 2. Cohen, B.H. (2001). Explaining psychological statistics (2nd ed.). New York, NY: John Wiley & Sons. Constant, D., Kiesler, S., & Sproull, L. (1994). What’s mine is ours, or is it? A study of attitudes about information sharing. Information Systems Research, 5(4), 400–421. doi:10.1287/isre.5.4.400 Cozby, P.C. (1972). Self-disclosure, reciprocity and liking. Sociometry, 35(1), 151–160. doi:10.2307/2786555 Davenport, E., & Hall, H. (2002). Organizational knowledge and communities of practice. Annual Review of Information Science & Technology, 36(1), 171–227. Dimicco, J., Millen, D.R., Geyer, W., Dugan, C., Brownholtz, B., Muller, M., & Street, R. (2008). Motivations for social networking at work. In Proceedings of the 2008 ACM conference on Computer Supported Cooperative Work (pp. 711–720). San Diego, CA: ACM. IJSC Vol. 8, No. 2, 2015


Dimmick, J., Chen, Y., & Li, Z. (2004). Competition between the Internet and traditional news media: The gratification-opportunities niche dimension. Journal of Media Economics, 17(1), 19–33. doi:10.1207/s15327736me1701_2 Frederick, E.L., Lim, C.H., Clavio, G., & Walsh, P. (2012). Why we follow: An examination of parasocial interaction and fan motivations for following athlete archetypes on Twitter. International Journal of Sport Communication, 5(4), 481–502. Gil de Zúñiga, H., Veenstra, A., Vraga, E., & Shah, D. (2010). Digital democracy: Reimagining pathways to political participation. Journal of Information Technology & Politics, 7(1), 36–51. doi:10.1080/19331680903316742 Glaser, B.G. (1965). The constant comparative method of qualitative analysis. Social Problems, 12(4), 436–445. doi:10.2307/798843 Glaser, B.G., & Strauss, A. (1967). The discovery of grounded theory. London, UK: Weidenfeld & Nicholson. Glynn, C.J., Huge, M.E., & Hoffman, L.H. (2012). All the news that’s fit to post: A profile of news use on social networking sites. Computers in Human Behavior, 28(1), 113–119. doi:10.1016/j.chb.2011.08.017 Hambrick, M.E., & Mahoney, T.Q. (2011). “It’s incredible—trust me’: Exploring the role of celebrity athletes as marketers in online social networks. International Journal of Sport Management and Marketing, 10(3/4), 161–179. doi:10.1504/IJSMM.2011.044794 Hambrick, M.E., Simmons, J.M., Greenhalgh, G.P., & Greenwell, T.C. (2010). Understanding professional athletes’ use of Twitter: A content analysis of athlete tweets. International Journal of Sport Communication, 3(4), 454–471. Hanson, G., & Haridakis, P. (2008). YouTube users watching and sharing the news: A uses and gratifications approach. Journal of Electronic Publishing, 11(3), 1–15. doi:10.3998/3336451.0011.305 Hutchins, B. (2011). The acceleration of media sport culture: Twitter, telepresence and online messaging. Information Communication and Society, 14(2), 237–257. doi:10. 1080/1369118X.2010.508534 Israel, S. (2009). Twitterville: How businesses can thrive in the new global neighborhoods. New York, NY: Penguin. Jackoway, A., Samet, H., & Sankaranarayanan, J. (2011). Identification of live news events using Twitter. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks (pp. 25–32). New York, NY: ACM. Kassing, J.W., & Sanderson, J. (2010). Fan–athlete interaction and Twitter tweeting through the Giro: A case study. International Journal of Sport Communication, 3(1), 113–128. Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? In Proceedings of the 19th International Conference on World Wide Web (pp. 591–600). New York, NY: ACM. Lee, C.S., & Ma, L. (2012). News sharing in social media: The effect of gratifications and prior experience. Computers in Human Behavior, 28(2), 331–339. doi:10.1016/j. chb.2011.10.002 Lerman, K., & Ghosh, R. (2010). Information contagion: An empirical study of the spread of news on digg and Twitter social networks. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (pp. 90–97). Palo Alto, CA: AAAI. Lindlof, T.T.R., & Taylor, B.C. (2010). Qualitative communication research methods. Thousand Oaks, CA: Sage. Liu, Z., Liu, L., & Li, H. (2012). Determinants of information retweeting in microblogging. Internet Research, 22(4), 443–466. doi:10.1108/10662241211250980 Ma, L., Lee, C.S., & Goh, D.H. (2011). That’s news to me: The influence of perceived gratifications and personal experience on news sharing in social media. In Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries (pp. 141–144). New York, NY: ACM.

IJSC Vol. 8, No. 2, 2015


Macskassy, S.A., & Michelson, M. (2012). Why do people retweet? Anti-homophily wins the day! In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM) (pp. 209–216). Dublin, Ireland: AAAI. Matsa, K.E., & Mitchell, A. (2014). 8 key takeaways about social media and news. Washington, DC: Pew Research Center’s Project for Excellence in Journalism. Nagarajan, M., Purohit, H., & Sheth, A. (2010). A qualitative examination of topical tweet and retweet practices. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (pp. 295–298). Palo Alto, CA: AAAI. Pegoraro, A. (2010). Look who’s talking—Athletes on Twitter: A case study. International Journal of Sport Communication, 3(4), 501–514. Pilerot, O., & Limberg, L. (2011). Information sharing as a means to reach collective understanding: A study of design scholars’ information practices. Journal of Documentation, 67(2), 312–333. doi:10.1108/00220411111109494 Reed, S. (2013). American sports writers’ social media use and its influence on professionalism. Journalism Practice, 7(5), 555–571. doi:10.1080/17512786.2012.739325 Riffe, D., Lacy, S., & Fico, F. (2014). Analyzing media messages: Using quantitative content analysis in research (3rd ed.). New York, NY: Routledge. Roberts, C. (October 2013). Twitter in the pressbox: How social media affects the game-day print product of sportswriters. Paper presented at the “Beyond Convergence: Mobile, Social, and Virtual Media” Conference, Las Vegas, NV. Rudat, A., Buder, J., & Hesse, F.W. (2014). Audience design in Twitter: Retweeting behavior between informational value and followers’ interests. Computers in Human Behavior, 35, 132–139. http://dx.doi.org/10.1016/j.chb.2014.03.006 Saldana, J. (2012). The coding manual for qualitative researchers. Thousand Oaks, CA: Sage. Sanderson, J. (2011). To tweet or not to tweet: exploring Division I athletic departments’ social-media policies. International Journal of Sport Communication, 4(4), 492–513. Sanderson, J., & Hambrick, M.E. (2012). Covering the scandal in 140 characters: A case study of Twitter’s role in coverage of the Penn State saga. International Journal of Sport Communication, 5(3), 384–402. Savolainen, R. (2007). Motives for giving information in non‐work contexts and the expectations of reciprocity. The case of environmental activists. Proceedings of the American Society for Information Science and Technology, 44(1), 1–13. doi:10.1002/ meet.1450440210 Savolainen, R. (2009). Small world and information grounds as contexts of information seeking and sharing. Library & Information Science Research, 31(1), 38–45 Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0740818808001369. doi:10.1016/j. lisr.2008.10.007 Sharratt, M., & Usoro, A. (2003). Understanding knowledge-sharing in online communities of practice. Electronic Journal of Knowledge Management, 1(2), 187–196. Sheffer, M.L., & Schultz, B. (2010). Paradigm shift or passing fad? Twitter and sports journalism. International Journal of Sport Communication, 3(4), 472–484. Sousa, D., Sarmento, L., & Mendes Rodrigues, E. (2010). Characterization of the twitter@ replies network: Are user ties social or topical? In Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents (SMUC) (pp. 63–70). Toronto, Canada: AAAI. Suh, B., Hong, L., Pirolli, P., & Chi, E.H. (2010). Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. In Proceedings of the 2010 IEEE Second International Conference on Social Computing (pp. 177–184). Washington, DC: IEEE. Talja, S., & Hansen, P. (2006). Information sharing. In A. Spink & C. Cole (Eds.), New directions in human information behavior (pp. 113–134). Amsterdam, The Netherlands: Springer Netherlands.

IJSC Vol. 8, No. 2, 2015


Tracy, S.J. (2012). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact. New York, NY: John Wiley & Sons. Van Den Hooff, B., & De Ridder, J. (2004). Knowledge sharing in context: The influence of organizational commitment, communication climate and CMC use on knowledge sharing. Journal of Knowledge Management, 8(6), 117–130. doi:10.1108/13673270410567675 Wang, X., Liu, H., Zhang, P., & Li, B. (2013). Identifying information spreaders in Twitter follower networks. In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining. Rome, Italy: ACM. Witkemper, C., Lim, C.H., & Waldburger, A. (2012). Social media and sports marketing: Examining the motivations and constraints of Twitter users. Sport Marketing Quarterly, 21(3), 170–183. Yang, J., & Counts, S. (2010). Predicting the speed, scale, and range of information diffusion in Twitter. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (Vol. 10, pp. 355–358). Dublin, Ireland: AAAI.

IJSC Vol. 8, No. 2, 2015