Dynamics of news events and social media reaction

The analysis of social sentiment expressed on the Web is becoming increasingly relevant to a variety of applications, and it is important to understand the underlying mechanisms which drive the evolution of sentiments in one way or another, in order to be able to predict these changes in the future. In this paper, we study the dynamics of news events and their relation to changes of sentiment expressed on relevant topics. We propose a novel framework, which models the behavior of news and social media in response to events as a convolution between event's importance and media response function, specific to media and event type. This framework is suitable for detecting time and duration of events, as well as their impact and dynamics, from time series of publication volume. These data can greatly enhance events analysis; for instance, they can help distinguish important events from unimportant, or predict sentiment and stock market shifts. As an example of such application, we extracted news events for a variety of topics and then correlated this data with the corresponding sentiment time series, revealing the connection between sentiment shifts and event dynamics.

[1]  Mikalai Tsytsarau.  Scalable Detection of Sentiment-Based Contradictions , 2011 .

[2]  Didier Sornette,et al.  Robust dynamic classes revealed by measuring the response function of a social system , 2008, Proceedings of the National Academy of Sciences.

[3]  Bernardo A. Huberman,et al.  Trends in Social Media: Persistence and Decay , 2011, ICWSM.

[4]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

[5]  Themis Palpanas,et al.  Scalable discovery of contradictions on the web , 2010, WWW '10.

[6]  Huan Liu,et al.  Exploiting social relations for sentiment analysis in microblogging , 2013, WSDM.

[7]  David Lazer,et al.  Voices of victory: a computational focus group framework for tracking opinion shift in real time , 2013, WWW '13.

[8]  K. Gaikovich.  Inverse Problems In Physical Diagnostics , 2004 .

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

[10]  Arvid Kappas,et al.  Sentiment in short strength detection informal text , 2010, J. Assoc. Inf. Sci. Technol..

[11]  D. Sornette,et al.  Endogenous Versus Exogenous Shocks in Complex Networks: An Empirical Test Using Book Sale Rankings , 2003, Physical review letters.

[12]  Mike Thelwall,et al.  Sentiment in Twitter events , 2011, J. Assoc. Inf. Sci. Technol..

[13]  Jure Leskovec,et al.  Meme-tracking and the dynamics of the news cycle , 2009, KDD.

[14]  Carlos Castillo,et al.  Investigating query bursts in a web search engine , 2013, Web Intell. Agent Syst..

[15]  Fang Wu,et al.  Novelty and collective attention , 2007, Proceedings of the National Academy of Sciences.

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

[17]  Themis Palpanas,et al.  Survey on mining subjective data on the web , 2011, Data Mining and Knowledge Discovery.

[18]  Sihem Amer-Yahia,et al.  Efficient sentiment correlation for large-scale demographics , 2013, SIGMOD '13.