Detecting tension in online communities with computational Twitter analysis

Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X, Rana, Omer Farooq ORCID: https://orcid.org/0000-0003-3597-2646, Avis, Nicholas John, Williams, Matthew Leighton ORCID: https://orcid.org/0000-0003-2566-6063, Housley, William ORCID: https://orcid.org/0000-0003-1568-9093, Edwards, Adam Michael ORCID: https://orcid.org/0000-0002-1332-5934, Morgan, Jeffrey and Sloan, Luke ORCID: https://orcid.org/0000-0002-9458-9332 2015. Detecting tension in online communities with computational Twitter analysis. Technological Forecasting & Social Change 95 , pp. 96-108. 10.1016/j.techfore.2013.04.013

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Abstract

The growing number of people using social media to communicate with others and document their personal opinion and action is creating a significant stream of data that provides the opportunity for social scientists to conduct online forms of research, providing an insight into online social formations. This paper investigates the possibility of forecasting spikes in social tension – defined by the UK police service as “any incident that would tend to show that the normal relationship between individuals or groups has seriously deteriorated” – through social media. A number of different computational methods were trialed to detect spikes in tension using a human coded sample of data collected from Twitter, relating to an accusation of racial abuse during a Premier League football match. Conversation analysis combined with syntactic and lexicon-based text mining rules; sentiment analysis; and machine learning methods was tested as a possible approach. Results indicate that a combination of conversation analysis methods and text mining outperforms a number of machine learning approaches and a sentiment analysis tool at classifying tension levels in individual tweets.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Cardiff Centre for Crime, Law and Justice (CCLJ) Computer Science & Informatics Social Sciences (Includes Criminology and Education)
Subjects:	H Social Sciences > H Social Sciences (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Elsevier
ISSN:	0040-1625
Funders:	JISC, ESRC
Date of Acceptance:	19 April 2013
Last Modified:	24 Nov 2024 22:22
URI:	https://orca.cardiff.ac.uk/id/eprint/47148

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