Modeling collective mood states from large-scale social media data


Modeling collective mood states from large-scale social media data

 Johan Bollen (Indiana University)


Online social networking services now function as a medium for the exchange of personal as well as public information for hundreds of millions of individuals. Advances in natural language processing now allow us to tap into that reservoir of psycho-social data, and perform computational social science in realtime. In this presentation I will provide an overview of existing text analysis approaches that have been used to extract indicators of social opinion and sentiment from social media data. Researchers have used these techniques to gauge "national happiness" as well as consumer sentiment towards particular brands and products. Perhaps most tantalizing, evidence has been found that social media feeds may contain predictive information with regards to a variety of socio-economic phenomena, such as movie box office receipts, product adoption rates, elections, and even stock market fluctuations. With respect to the latter, I will outline our own research on the subject of stock market prediction. My team and I have analyzed large-scale Twitter data to assess daily fluctuations of the public's mood state. We found that these fluctuations contain predictive information with regards to the up and down movements of broad market indices, such as the Dow Jones Industrial Average.


The Speaker:
Johan Bollen is associate professor at the Indiana University School of Informatics and Computing where he is a member of the Center for Complex Networks and Systems and the Cognitive Science Program. He was formerly a staff scientist at the Los Alamos National Laboratory from 2005-2009, and an Assistant Professor at the Department of Computer Science of Old Dominion University from 2002 to 2005. He obtained his PhD in Experimental Psychology from the University of Brussels (VUB) in 2001 on the subject of cognitive models of human hypertext navigation. He has taught courses on Informatics, Data Mining, Information Retrieval, and Digital Libraries. His research has been funded by the IARPA, NSF, the Andrew W. Mellon Foundation, Library of Congress, NASA, and the Los Alamos National Laboratory. His present research interests are computational social science, web science, behavioral finance, and informetrics. Johan lives in Bloomington, Indiana with his wife and daughter.


Video of the talk: