Prédictions d'activité dans les réseaux sociaux en ligne

Online Platforms dedicated to social networking host new social phenomenons. Thus several keywords may suddenly take an unprecedented importance, reflecting the number of dis- cussions they have raised within a short time period. Such bursts in topic discussions are usually referred to as buzz events. We address in this paper the problem of predicting the activity volume associated to a given keyword without a priori knowledge on the underlying social network. To do so, we propose to define social netowrk on a content-centric way. Our approach is evaluated at "industrial scale" on two different social networks: Twitter, a platform with extremely fast dynamics (Kwak et al., 2010), and Tom's Hardware, a worldwide forum network focusing on new technology. The experiments conducted reveal that it is possible to predict activity volume associated to a keyword in social media with high accuracy.

[1]  A. F. Bissell,et al.  Cusum Techniques for Quality Control , 1969 .

[2]  Jon M. Kleinberg,et al.  Bursty and Hierarchical Structure in Streams , 2002, Data Mining and Knowledge Discovery.

[3]  Shirley Coleman,et al.  CUSUM method in predicting regime shifts and its performance in different stock markets allowing for transaction fees , 2006 .

[4]  Nokuthaba Sibanda,et al.  The CUSUM chart method as a tool for continuous monitoring of clinical outcomes using routinely collected data , 2007, BMC medical research methodology.

[5]  Thomas Philip Runarsson,et al.  Support vector machines and dynamic time warping for time series , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[6]  Lada A. Adamic,et al.  Knowledge sharing and yahoo answers: everyone knows something , 2008, WWW.

[7]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[8]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[9]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[10]  Ralf Herbrich,et al.  Predicting Information Spreading in Twitter , 2010 .

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

[12]  Aron Culotta,et al.  Towards detecting influenza epidemics by analyzing Twitter messages , 2010, SOMA '10.

[13]  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.

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

[15]  Jure Leskovec,et al.  Patterns of temporal variation in online media , 2011, WSDM '11.

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

[17]  Hakim Hacid,et al.  A predictive model for the temporal dynamics of information diffusion in online social networks , 2012, WWW.

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

[19]  Christos Faloutsos,et al.  Rise and fall patterns of information diffusion: model and implications , 2012, KDD.

[20]  Gilad Mishne,et al.  A Study of "Churn" in Tweets and Real-Time Search Queries , 2012, ICWSM.

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

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