Understanding Professional Athletes’ Use of Twitter: A Content Analysis of Athlete Tweets

2010 ◽  
Vol 3 (4) ◽  
pp. 454-471 ◽  
Author(s):  
Marion E. Hambrick ◽  
Jason M. Simmons ◽  
Greg P. Greenhalgh ◽  
T. Christopher Greenwell

The online social network Twitter has grown exponentially since 2008. The current study examined Twitter use among professional athletes who use Twitter to communicate with fans and other players. The study used content analysis to place 1,962 tweets by professional athletes into one of six categories: interactivity, diversion, information sharing, content, promotional, and fanship. Many of the tweets fell into the interactivity category (34%). Athletes used Twitter to converse directly with their followers. Those with the most followers had more interactivity tweets. A large percentage of tweets (28%) fell into the diversion category, because many of the tweets involved non-sports-related topics, and relatively few of the tweets (15%) involved players discussing their own teams or sports. In addition, only 5% of the tweets were promotional in nature, indicating that professional athletes may not be taking advantage of the promotional opportunities Twitter may provide.

2021 ◽  
Author(s):  
V.V. Vasilkova ◽  
N.I. Legostaeva

Nowadays, in the field of social bots investigations, we can observe a new research trend — a shift from a technology-centered to sociology-centered interpretations. It leads to the creation of new perspectives for sociology: now the phenomenon of social bots is not only considered as one of the efficient manipulative technologies but has a wider meaning: new communicative technologies have an informational impact on the social networks space. The objective of this research is to assess the new approaches of the established social bots typologies (based on the fields of their usage, objectives, degree of human behavior imitation), and also consider the ambiguity and controversy of the use of such typologies using the example of botnets operating in the VKontakte social network. A method of botnet identification is based on comprehensive methodology developed by the authors which includes the frequency analysis of published messages, botnet profiling, statistical analysis of content, analysis of botnet structural organization, division of content into semantic units, forming content clusters, content analysis inside the clusters, identification of extremes — maximum number of unique texts published by botnets in a particular cluster for a certain period. The methodology was applied for the botnet space investigation of Russian online social network VKontakte in February and October 2018. The survey has fixed that among 10 of the most active performing botnets, three botnets were identified that demonstrate the ambiguity and controversy of their typologization according to the following criteria: botnet “Defrauded shareholders of LenSpetsStroy” — according to the field of their usage, botnet “Political news in Russian and Ukrainian languages” — according to their objectives, botnet “Ksenia Sobchak” — according to the level of human behavior imitation. The authors identified the prospects for sociological analysis of different types of bots in a situation of growing accessibility and routinization of bot technologies used in social networks. Keywords: social bots, botnets, classification, VKontakte social network


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ana Suárez Vázquez ◽  
Manuel Chica Serrano

PurposeThis paper aims to fill a gap in the existing literature by answering the following question: is the effect of envy on people's intention to share information the same in offline settings and on online social networks?Design/methodology/approachTwo studies demonstrate (1) how envy that results from upward social comparisons affects people's intention to share information and (2) the difference between online and offline settings.FindingsThe likelihood of sharing information susceptible of triggering envy is lower in online social networks than in an offline scenario.Research limitations/implicationsIn digital environments, feelings of envy depend on the number of social comparisons that the individual is exposed to.Practical implicationsThis research recommends (1) incorporating tools that allow online social network users to feel part of their network's successes, (2) promoting offline diffusion of information and (3) encouraging people to play an active role when using online social networks.Social implicationsBenefits can be derived from offering tools that permit receivers to take advantage of the selective self-presentation of other users. Such tools could have positive consequences for the welfare of online social network users.Originality/valueTo date, the literature has paid no attention to envy as an engine of information sharing. This aspect is especially relevant when discussing platforms whose main goal is precisely information sharing and that offer fertile ground for upward social comparisons.


2019 ◽  
Vol 49 (1) ◽  
pp. 203-217 ◽  
Author(s):  
Young-joo Lee

The younger generation’s widespread use of online social network sites has raised concerns and debates about social network sites’ influence on this generation’s civic engagement, whether these sites undermine or promote prosocial behaviors. This study empirically examines how millennials’ social network site usage relates to volunteering, using the 2013 data of the Minnesota Adolescent Community Cohort Study. The findings reveal a positive association between a moderate level of Facebook use and volunteering, although heavy users are not more likely to volunteer than nonusers. This bell-shaped relationship between Facebook use and volunteering contrasts with the direct correlation between participation in off-line associational activities and volunteering. Overall, the findings suggest that it is natural to get mixed messages about social network sites’ impacts on civic engagement, and these platforms can be useful tools for getting the word out and recruiting episodic volunteers.


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