Detecting Tension in Online Twitter Communities with Computational Twitter Analysis

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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 throughout this article as a possible approach. The research team’s 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.


This research and its suggestions are particularly useful to researchers interesting in work around detecting tensions on social media, particularly of an extremist nature.


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Pete Burnap, Omer F. Rana, Nick Avis, Matthew Williams, William Housley, Adam Edwards, Jeffrey Morgan, and Luke Sloan