This paper explores the potential of utilizing social media data to forecast spikes in social tension, as defined by incidents indicating a severe deterioration in the relationship between individuals or groups. Focusing on a case study of an accusation of racial abuse during a Premier League football match, the study employs various computational methods to analyze a human-coded sample of Twitter data. Techniques tested include conversation analysis combined with syntactic and lexicon-based text mining rules, sentiment analysis, and machine learning. The findings suggest that a blend of conversation analysis and text mining techniques is more effective at classifying tension levels in tweets compared to certain machine learning methods and a sentiment analysis tool.
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Burnap, Pete, et al. "Detecting tension in online communities with computational Twitter analysis." Technological Forecasting and Social Change 95 (2015): 96-108.
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