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

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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Charitonidis, C, Rashid, A & Taylor, PJ 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. https://doi.org/10.1145/2808797.2809332

APA

Charitonidis, C., Rashid, A., & Taylor, P. 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 (pp. 864-871). (ASONAM '15). ACM. https://doi.org/10.1145/2808797.2809332

Vancouver

Charitonidis C, Rashid A, Taylor PJ. 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. New York: ACM. 2015. p. 864-871. (ASONAM '15). doi: 10.1145/2808797.2809332

Author

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

Bibtex

@inproceedings{5405cc46eb4d4ad282c36972d4bf9ed7,
title = "Weak signals as predictors of real-world phenomena in social media",
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.",
keywords = "Social media, Twitter, Collective action, London riots, Weak signals, Forecasting, Early detection, Content analysis",
author = "Christos Charitonidis and Awais Rashid and Taylor, {Paul J.}",
year = "2015",
doi = "10.1145/2808797.2809332",
language = "English",
isbn = "9781450338547",
series = "ASONAM '15",
publisher = "ACM",
pages = "864--871",
booktitle = "ASONAM '15 Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015",

}

RIS

TY - GEN

T1 - Weak signals as predictors of real-world phenomena in social media

AU - Charitonidis, Christos

AU - Rashid, Awais

AU - Taylor, Paul J.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Social media

KW - Twitter

KW - Collective action

KW - London riots

KW - Weak signals

KW - Forecasting

KW - Early detection

KW - Content analysis

U2 - 10.1145/2808797.2809332

DO - 10.1145/2808797.2809332

M3 - Conference contribution/Paper

SN - 9781450338547

T3 - ASONAM '15

SP - 864

EP - 871

BT - ASONAM '15 Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

PB - ACM

CY - New York

ER -