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.
FP7-ICT Collaborative Project ICT-2013-611233 PHEME Deliverable D2.1 (WP2) This document outlines a preliminary definition of an annotation scheme for rumours spread through social media, as well as the code frames that will be used to mark up the corpora collected for PHEME. It has been developed through an iterative process of revisions, and it is intended to encompass the different kinds of rumours that can be spread and discussed in the context of different events and situations. It especially considers the way conversations flow in social media, and has been developed in an interdisciplinary style by building on work in sociolinguistics, including the related approaches of Conversation Analysis [51] and Ethnomethodology [16]. The resulting annotation scheme will be used for the annotation of a larger corpora of social media rumours through a crowdsourcing platform. Annotation guidelines will be defined in following work and tested with small social media corpora before running the large annotation task. Keyword list: rumors, veracity, annotation scheme, social media A rumor is commonly defined as a statement whose true value is unverifiable. Rumors may spread misinformation (false information) or disinformation (deliberately false information) on a network of people. Identifying rumors is crucial in online social media where large amounts of information are easily spread across a large network by sources with unverified authority. In this paper, we address the problem of rumor detection in microblogs and explore the effectiveness of 3 categories of features: content-based, network-based, and microblog-specific memes for correctly identifying rumors. Moreover, we show how these features are also effective in identifying disinformers, users who endorse a rumor and further help it to spread. We perform our experiments on more than 10,000 manually annotated tweets collected from Twitter and show how our retrieval model achieves more than 0.95 in Mean Average Precision (MAP). Finally, we believe that our dataset is the first large-scale dataset on rumor detection. It can open new dimensions in analyzing online misinformation and other aspects of microblog conversations.