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

Christos Charitonidis, Awais Rashid, Paul J. Taylor

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

8 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Subtitle of host publicationASONAM 2015: Paris, France, August 25-28, 2015
EditorsJian Pei, Fabrizio Silvestri, Jie Tang
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Electronic)9781450338547
ISBN (Print)9781509020942
Publication statusPublished - 2015
Externally publishedYes

Publication series

NameASONAM '15


  • Social media
  • Twitter
  • Collective action
  • London riots
  • Weak signals
  • Forecasting
  • Early detection
  • Content analysis


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