Factors Affecting Retweetability: An Event-Centric Analysis on Twitter

In Twitter information primarily propagates through retweet mechanism. While a massive amount of tweets gets generated everyday, only a handful of them get retweeted widely. In this study, we have investigated the impact of user-roles in retweet phenomena. We have introduced the concept of “Information Diffusion Impact” (IDI) and identified three important user roles, namely “information starter”, “amplifier”, and “transmitter”. Retweetability has been modeled using IDI impact for different user roles along with the content features like presence of hashtag, URL etc. Further, the effect of a major event on the factors affecting retweetability has been investigated. Our findings demonstrate that retweetability is significantly affected by amplifiers and informationstarters and these effects change substantially due to event. We have also reexamined our model in another dataset of the Boston marathon bomb blast, 2013 and the outcome of this analysis is in good agreement with our findings from Japan earthquake dataset.

[1]  H. Rao,et al.  Twitter as a Rapid Response News Service: An Exploration in the Context of the 2008 China Earthquake , 2010, Electron. J. Inf. Syst. Dev. Ctries..

[2]  Qi Gao,et al.  Analyzing user modeling on twitter for personalized news recommendations , 2011, UMAP'11.

[3]  Santo Fortunato,et al.  Finding Statistically Significant Communities in Networks , 2010, PloS one.

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

[5]  Ed H. Chi,et al.  Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network , 2010, 2010 IEEE Second International Conference on Social Computing.

[6]  Leysia Palen,et al.  Twitter adoption and use in mass convergence and emergency events , 2009 .

[7]  Ramanathan V. Guha,et al.  Information diffusion through blogspace , 2004, SKDD.

[8]  Danah Boyd,et al.  Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[9]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Barbara Poblete,et al.  Twitter under crisis: can we trust what we RT? , 2010, SOMA '10.

[11]  Thomas Gottron,et al.  Bad news travel fast: a content-based analysis of interestingness on Twitter , 2011, WebSci '11.

[12]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[13]  Nargis Pervin,et al.  Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams , 2013, TMIS.

[14]  Satoshi Kurihara,et al.  Information sharing on Twitter during the 2011 catastrophic earthquake , 2013, WWW.

[15]  Marco Toledo Bastos,et al.  What Sticks With Whom? Twitter Follower-Followee Networks and News Classification , 2012, ICWSM 2012.

[16]  Les Carr,et al.  Identifying communicator roles in twitter , 2012, WWW.

[17]  Eni Mustafaraj,et al.  Visualizing co-retweeting behavior for recommending relevant real-time content , 2013, MSM '13.

[18]  Sushil Jajodia,et al.  Who is tweeting on Twitter: human, bot, or cyborg? , 2010, ACSAC '10.

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

[20]  Scott Counts,et al.  Predicting the Speed, Scale, and Range of Information Diffusion in Twitter , 2010, ICWSM.

[21]  Juan-Zi Li,et al.  Understanding retweeting behaviors in social networks , 2010, CIKM.

[22]  Duncan J. Watts,et al.  Who says what to whom on twitter , 2011, WWW.