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Weak signals as predictors of real-world phenomena in social media

Research output: Contribution in Book/Report/ProceedingsPaper

Published
Publication date2015
Host publicationASONAM '15 Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015
Place of PublicationNew York
PublisherACM
Pages864-871
Number of pages8
ISBN (Print)9781450338547
<mark>Original language</mark>English

Publication series

NameASONAM '15

Abstract

Global and national events in recent years have shown that online social media can be a force for good (e.g., Arab Spring) and harm (e.g., the London riots). In both of these examples, social media played a key role in group formation and organization, 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.