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

Charitonidis, Christos and Rashid, Awais and Taylor, Paul J. (2015) Weak signals as predictors of real-world phenomena in social media. In: ASONAM '15 Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. ASONAM '15 . ACM, New York, pp. 864-871. ISBN 9781450338547

[img]
Preview
PDF (CharitonidisRashidTaylor)
CharitonidisRashidTaylor.pdf - Accepted Version

Download (3MB)

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.

Item Type: Contribution in Book/Report/Proceedings
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
Faculty of Science and Technology > Psychology
ID Code: 82043
Deposited By: ep_importer_pure
Deposited On: 08 Oct 2016 01:47
Refereed?: Yes
Published?: Published
Last Modified: 24 Feb 2020 04:29
URI: https://eprints.lancs.ac.uk/id/eprint/82043

Actions (login required)

View Item View Item