On the Real-time Prediction Problems of Bursting Hashtags in Twitter

Hundreds of thousands of hashtags are generated every day on Twitter. Only a few become bursting topics. Among the few, only some can be predicted in real-time. In this paper, we take the initiative to conduct a systematic study of a series of challenging real-time prediction problems of bursting hashtags. Which hashtags will become bursting? If they do, when will the burst happen? How long will they remain active? And how soon will they fade away? Based on empirical analysis of real data from Twitter, we provide insightful statistics to answer these questions, which span over the entire lifecycles of hashtags.

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

[2]  Brian D. Davison,et al.  Predicting popular messages in Twitter , 2011, WWW.

[3]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[4]  Ari Rappoport,et al.  What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities , 2012, WSDM '12.

[5]  Lei Yang,et al.  We know what @you #tag: does the dual role affect hashtag adoption? , 2012, WWW.

[6]  Brendan T. O'Connor,et al.  Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters , 2013, NAACL.

[7]  Miles Osborne,et al.  RT to Win! Predicting Message Propagation in Twitter , 2011, ICWSM.

[8]  Jiawei Han,et al.  Predicting future popularity trend of events in microblogging platforms , 2012, ASIST.

[9]  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.

[10]  David Lazer,et al.  #Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtags , 2013, ICWSM.

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

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

[13]  Ling Feng,et al.  Predicting lifespans of popular tweets in microblog , 2012, SIGIR '12.

[14]  Brian D. Davison,et al.  Co-factorization machines: modeling user interests and predicting individual decisions in Twitter , 2013, WSDM.

[15]  Devavrat Shah,et al.  A Latent Source Model for Nonparametric Time Series Classification , 2013, NIPS.

[16]  Isabell M. Welpe,et al.  Election Forecasts With Twitter , 2011 .

[17]  Gao Cong,et al.  On predicting the popularity of newly emerging hashtags in Twitter , 2013, J. Assoc. Inf. Sci. Technol..

[18]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[19]  Zhe Zhao,et al.  Questions about questions: an empirical analysis of information needs on Twitter , 2013, WWW.

[20]  M. Osborne,et al.  Using Prediction Markets and Twitter to Predict a Swine Flu Pandemic , 2009 .

[21]  Ramanathan V. Guha,et al.  The predictive power of online chatter , 2005, KDD '05.

[22]  Hsia-Ching Chang,et al.  A new perspective on Twitter hashtag use: Diffusion of innovation theory , 2010, ASIST.

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

[24]  Chenhao Tan,et al.  On the Interplay between Social and Topical Structure , 2011, ICWSM.

[25]  Stanislav Nikolov.  Trend or no trend : a novel nonparametric method for classifying time series , 2012 .

[26]  Gao Cong,et al.  Will this #hashtag be popular tomorrow? , 2012, SIGIR '12.