Networked Fandom: Applying Systems Theory to Sport Twitter Analysis

Abstract

The purpose of this study was to employ systems theory to analyze the social network of a Big Ten football team’s Twitter community. An identifiable network was found among the observed actors (N = 139), with fan accounts composing the largest percentage of the network. The number of observed reciprocal interactions was low, only 11.8% of the interactions and only 21.5% of the nodes. Traditionalmedia accounts frequently interacted with other media accounts, while fans interacted primarily with other fans. Overall, nontraditional-media accounts’ users were most focused on interactivity. Team-related accounts were almost nonexistent in the interactive network. A systems-theory-based network was found in terms of input, transformation, and output components. The feedback loop was the weak link in the network, indicating a possible lack of importance of direct feedback in Twitter social networks.

Sport and social media research: A review

“…Exchange between brands and consumers via social media aligns with the interactive nature of relationships advanced by Vargo and Lusch (2004), while demonstrating that relationship building occurs through a process that is dyadic, interactive and meaningful (Brodie et al, 2013). In addition, the networks of users revealed through social network analysis of a brand's social media followers (e.g., Clavio et al, 2012) suggests that engagement with these platforms can facilitate meaningful relationships among communities of consumers (Hatch & Schultz, 2010). Collectively, the importance placed on using social media to foster relationships demonstrates that these platforms are critical tools for mutual exchange (Grönroos, 2004).…”

“…Sanderson and Hambrick (2012) classified these preferences, finding that journalists use Twitter to offer commentary, break news, and interact with other journalists. Clavio et al (2012) discussed the interaction among journalists via Twitter in a social network analysis of a NCAA football team's Twitter followers. Traditional and non-traditional media members were the most active users, and these users existed within subgroups among the followers.…”

The Multiple In-group Identity Framework

“…Such research can provide new insights into the effects of different in-group identities on consumer loyalty and behaviour. Prior research using social network analysis to explore subgroup patterns emerging via social media is a promising start for research of this kind (Clavio, Burch, & Frederick, 2012).…”

Keep the Ball Rolling: Information Diffusion within Large Sports-Related Networks through Social Mediators

“…First, by examining online sports communities through a network lens, we are able to understand how information diffuses from one individual, group, or organization to another within social networks (Hambrick, 2013). Exploring this topic for both top division and first division clubs does not only extend the scope of sports communication research in general (see, e.g., Clavio, Burch, & Frederick, 2012) but contributes specifically to understanding the dynamics of online sports communities. Second, drawing from mediated public relations literature, we distinguished between five types of social mediators (i.e., organizational, industry, media, individual, and celebrity social mediators).…”

“…Second, drawing from mediated public relations literature, we distinguished between five types of social mediators (i.e., organizational, industry, media, individual, and celebrity social mediators). This is important, as it can help in understanding how social media has changed the information diffusion process (e.g., Clavio et al, 2012). Finally, we tested whether certain types of social mediators affect fan engagement on Twitter.…”