Concurrent Bursty Behavior of Social Sensors in Sporting Events

The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as “social sensors.” Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a strong indicator of winning or losing a game. More specifically, our simple tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately react to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. We confirm that these findings also hold true for tweets related to Major League Baseball games in 2015. Furthermore, we demonstrate active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of a substantial difference in user popularity as an information source. While previous studies have mainly focused on bursts of tweets as a simple indicator of a real-world event, the temporal correlation between tweets and retweets implies unique aspects of social sensors, offering new insights into human behavior in a highly connected world.

[1]  Marieke van Erp,et al.  Automatic Extraction of Soccer Game Events from Twitter , 2012, DeRiVE@ISWC.

[2]  Scott A. Golder,et al.  Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures , 2011 .

[3]  Felix Tusa Can Shape a Protest Movement : The Cases of Egypt in 2011 and Iran in 2009 , 2013 .

[4]  Yamir Moreno,et al.  Structural and Dynamical Patterns on Online Social Networks: The Spanish May 15th Movement as a Case Study , 2011, PloS one.

[5]  A. Vespignani,et al.  Competition among memes in a world with limited attention , 2012, Scientific Reports.

[6]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[7]  Esteban Moro,et al.  Social Features of Online Networks: The Strength of Intermediary Ties in Online Social Media , 2011, PloS one.

[8]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[9]  Carlos J. Martín-Dancausa,et al.  Spot the Ball: Detecting Sports Events on Twitter , 2014, ECIR.

[10]  Ciro Cattuto,et al.  Dynamical classes of collective attention in twitter , 2011, WWW.

[11]  Nello Cristianini,et al.  Nowcasting Events from the Social Web with Statistical Learning , 2012, TIST.

[12]  G. Miller Sociology. Social scientists wade into the tweet stream. , 2011, Science.

[13]  A. Robinson I. Introduction , 1991 .

[14]  Filippo Menczer,et al.  Partisan asymmetries in online political activity , 2012, EPJ Data Science.

[15]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[16]  Alan F. Smeaton,et al.  Using Twitter to Detect and Tag Important Events in Live Sports , 2011 .

[17]  Johan Bollen,et al.  Happiness Is Assortative in Online Social Networks , 2011, Artificial Life.

[18]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[19]  Lin Zhong,et al.  Human as Real-Time Sensors of Social and Physical Events: A Case Study of Twitter and Sports Games , 2011, ArXiv.

[20]  Jon Kleinberg,et al.  Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter , 2011, WWW.

[21]  M. Kendall Statistical Methods for Research Workers , 1937, Nature.

[22]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[23]  Aaron Clauset,et al.  Scoring dynamics across professional team sports: tempo, balance and predictability , 2013, EPJ Data Science.

[24]  Kazuyuki Aihara,et al.  Quantifying Collective Attention from Tweet Stream , 2013, PloS one.

[25]  Ciro Cattuto,et al.  Mining Concurrent Topical Activity in Microblog Streams , 2014, #MSM.

[26]  S. Kiesler,et al.  IDENTITY AND BOND THEORIES TO UNDERSTAND DESIGN DECISIONS FOR ONLINE COMMUNITIES. , 2006 .

[27]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[28]  Rizal Setya Perdana What is Twitter , 2013 .

[29]  Christopher M. Danforth,et al.  Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter , 2011, PloS one.

[30]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[31]  Yamir Moreno,et al.  The Dynamics of Protest Recruitment through an Online Network , 2011, Scientific reports.

[32]  Albert-László Barabási,et al.  Collective Response of Human Populations to Large-Scale Emergencies , 2011, PloS one.