Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a rumourous conversation as either supporting, denying or questioning the rumour. Using a classifier based on Gaussian Processes, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will warn both ordinary users of Twitter and professional news practitioners when a rumour is being rebutted.
Another study that shows insightful conclusions with respect to stance towards rumours is that by @cite_17 . The authors conducted an analysis of a large dataset of tweets related to the riots in the UK, which took place in August 2011. The dataset collected in the riots study is one of the two used in our experiments, and we describe it in more detail in section . After grouping the tweets into topics, where each represents a rumour, they were manually categorised into different classes, namely:
@cite_17
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For social scientists, the widespread adoption of social media presents both an opportunity and a challenge. Data that can shed light on people’s habits, opinions and behaviour is available now on a scale never seen before, but this also means that it is impossible to analyse using conventional methodologies and tools. This article represents an experiment in applying a computationally assisted methodology to the analysis of a large corpus of tweets sent during the August 2011 riots in England.