Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
}
TY - CHAP
T1 - Predicting Collective Action from Micro-Blog Data
AU - Charitonidis, Christos
AU - Rashid, Awais
AU - Taylor, Paul J.
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2017/3/18
Y1 - 2017/3/18
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-51049-1_7
DO - 10.1007/978-3-319-51049-1_7
M3 - Chapter
SN - 9783319845531
T3 - Lecture Notes in Social Networks
SP - 141
EP - 170
BT - Prediction and Inference from Social Networks and Social Media
A2 - Kawash, Jalal
A2 - Agarwal, Nitin
A2 - Özyer, Tansel
PB - Springer, Cham
ER -