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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Social media are frequently rife with rumours, and the study of rumour conversational aspects can provide valuable knowledge about how rumours evolve over time and are discussed by others who support or deny them. In this work, we present a new annotation scheme for capturing rumour-bearing conversational threads, as well as the crowdsourcing methodology used to create high quality, human annotated datasets of rumourous conversations from social media. The rumour annotation scheme is validated through comparison between crowdsourced and reference annotations. We also found that only a third of the tweets in rumourous conversations contribute towards determining the veracity of rumours, which reinforces the need for developing methods to extract the relevant pieces of information automatically. As breaking news unfolds people increasingly rely on social media to stay abreast of the latest updates. The use of social media in such situations comes with the caveat that new information being released piecemeal may encourage rumours, many of which remain unverified long after their point of release. Little is known, however, about the dynamics of the life cycle of a social media rumour. In this paper we present a methodology that has enabled us to collect, identify and annotate a dataset of 330 rumour threads (4,842 tweets) associated with 9 newsworthy events. We analyse this dataset to understand how users spread, support, or deny rumours that are later proven true or false, by distinguishing two levels of status in a rumour life cycle i.e., before and after its veracity status is resolved. The identification of rumours associated with each event, as well as the tweet that resolved each rumour as true or false, was performed by journalist members of the research team who tracked the events in real time. Our study shows that rumours that are ultimately proven true tend to be resolved faster than those that turn out to be false. Whilst one can readily see users denying rumours once they have been debunked, users appear to be less capable of distinguishing true from false rumours when their veracity remains in question. In fact, we show that the prevalent tendency for users is to support every unverified rumour. We also analyse the role of different types of users, finding that highly reputable users such as news organisations endeavour to post well-grounded statements, which appear to be certain and accompanied by evidence. Nevertheless, these often prove to be unverified pieces of information that give rise to false rumours. Our study reinforces the need for developing robust machine learning techniques that can provide assistance in real time for assessing the veracity of rumours. The findings of our study provide useful insights for achieving this aim. Rumours on social media exhibit complex temporal patterns. This paper develops a model of rumour prevalence using a point process, namely a log-Gaussian Cox process, to infer an underlying continuous temporal probabilistic model of post frequencies. To generalize over different rumours, we present a multi-task learning method parametrized by the text in posts which allows data statistics to be shared between groups of similar rumours. Our experiments demonstrate that our model outperforms several strong baseline methods for rumour frequency prediction evaluated on tweets from the 2014 Ferguson riots. 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.