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Gaussian Processes for Rumour Stance Classification in Social Media

Michal Lukasik, Kalina Bontcheva, Trevor Cohn, Arkaitz Zubiaga, Maria Liakata, Rob Procter

ACM Transactions on Information Systems. 2019.

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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 conversation around a rumour as either supporting, denying or questioning the rumour. Using a Gaussian Process classifier, 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 show both ordinary users of Twitter and professional news practitioners how others orient to the disputed veracity of a rumour, with the final aim of establishing its actual truth value.
    title={Gaussian processes for rumour stance classification in social media},
    author={Lukasik, Michal and Bontcheva, Kalina and Cohn, Trevor and Zubiaga, Arkaitz and Liakata, Maria and Procter, Rob},
    journal={ACM Transactions on Information Systems (TOIS)},
    publisher={ACM New York, NY, USA}