A Sociolinguistic Route to the Characterization and Detection of the Credibility of Events on Twitter

Abstract

Although Twitter constitutes as one of the primary sources of real-time news with users acting as the sensors updating the content from all across the globe, yet the spread of rumours via Twitter is becoming an increasingly alarming issue and is known to have caused significant damage already. We propose a credibility analysis approach based on the linguistic structure of the tweets. We not only characterize the Twitter events but also predict their perceived credibility of them by a novel deep learning architecture. We use the huge CREDBANK data to conduct our experiments. Some of our exciting findings are that standard LIWC categories like 'negate', 'discrep', 'cogmech', 'swear' and the Empath categories like 'hate', 'poor', 'government', 'worship' and 'swearing-terms' correlate negatively with the credibility of events. While some of our results resonate with the earlier literature others represent novel insights of the fake and legitimate twitter events. Using the above observations and the current deep learning architecture we predict the credibility of an event (a four-class classification problem in our case) with an accuracy of 0.54 that improves the best-known state-of-the-art (current accuracy 0.43) by ~ 26%. A fascinating observation is that even by looking at the first few tweets of an event, it is possible to make the prediction almost as accurate as in the case where the entire volume of tweets is observed.