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.
In contrast with these three theories, Guerin and Miyazaki @cite_4 state that a rumour is a form of relationship-enhancing talk. Building on their previous work, they recall that many ways of talking serve the purpose of forming and maintaining social relationships. Rumours, they say, can be explained by such means.
@cite_4
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A conversational approach is developed to explain the ubiquitous presence of rumors, urban legends, and gossip as arising from their conversational properties rather than from side effects of cognitive processing or “effort after meaning.” It is suggested that the primary function of telling rumors, gossip, and urban legends is not to impart information to the listener or alleviate listener anxiety about the topic but to entertain or keep the listener’s attention, thereby enhancing social relationships. In this way, the traditional views of such stories are turned on their head, and an implication is that there is no essential feature of such stories just a range of conversational properties. The model also predicts hybrid forms that cannot be placed into one of the commonly named forms of talk. Some examples of these are given. The wider ramifications for changing “cognitive processing” effects into properties of social relationships are also drawn out.