|Burnap, Peter, Rana, Omer Farooq, Avis, Nicholas John, Williams, Matthew Leighton, Housley, William, Edwards, Adam Michael, Morgan, Jeffrey and Sloan, Luke 2015. Detecting tension in online communities with computational Twitter analysis. Technological Forecasting & Social Change 10.1016/j.techfore.2013.04.013|
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.
|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
|Last Modified:||17 Jan 2017 03:56|
Cited 16 times in Google Scholar. View in Google Scholar
Cited 16 times in Scopus. View in Scopus. Powered By Scopus® Data
Cited 1 time in Web of Science. View in Web of Science.
Actions (repository staff only)