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Prediction and Inference from Social Networks and Social Media

Part of the series Lecture Notes in Social Networks pp 141-170

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Predicting Collective Action from Micro-Blog Data

  • Christos CharitonidisAffiliated withSecurity Lancaster Research Centre, Infolab21, Lancaster University Email author 
  • , Awais RashidAffiliated withSecurity Lancaster Research Centre, Infolab21, Lancaster University
  • , Paul J. TaylorAffiliated withDepartment of Psychology, Centre for Research and Evidence on Security Threats (CREST), Lancaster University

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Abstract

Global and national events in recent years have shown that social media, and particularly micro-blogging services such as Twitter, can be a force for good (e.g., Arab Spring) and harm (e.g., London riots). In both of these examples, social media played a key role in group formation and organisation, and in the coordination of the group’s subsequent collective actions (i.e., the move from rhetoric to action). Surprisingly, despite its clear importance, little is understood about the factors that lead to this kind of group development and the transition to collective action. This paper focuses on an approach to the analysis of data from social media to detect weak signals, i.e., indicators that initially appear at the fringes, but are, in fact, early indicators of such large-scale real-world phenomena. Our approach is in contrast to existing research which focuses on analysing major themes, i.e., the strong signals, prevalent in a social network at a particular point in time. Analysis of weak signals can provide interesting possibilities for forecasting, with online user-generated content being used to identify and anticipate possible offline future events. We demonstrate our approach through analysis of tweets collected during the London riots in 2011 and use of our weak signals to predict tipping points in that context.

Keywords

Social media Micro-blogs Twitter Weak signals Forecasting Content analysis London riots Civil unrest Event detection Machine learning Predictive modelling Crisis Informatics