Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest. Here we introduce a novel approach that makes use of the sequence of transitions observed in tree-structured conversation threads in Twitter. The conversation threads are formed by harvesting users' replies to one another, which results in a nested tree-like structure. Previous work addressing stance classification task in Twitter has treated each tweet as a separate unit. Here we analyse tweets by virtue of their position in a sequence and test two sequential classifiers, Linear-Chain CRF and Tree CRF, each of which makes different assumptions about the conversation structure. We experiment with eight Twitter datasets, collected during breaking news, and show that exploiting the sequential structure of Twitter conversations achieves significant improvements over the non-sequential methods. Our work is the first to model Twitter conversations as a tree structure in this manner, introducing a novel way of tackling NLP tasks on Twitter conversations.
Our work presents different objectives in three aspects. First, we aim to classify the stance of tweets towards rumours that emerge while breaking news unfold; these rumours are unlikely to have been observed before, and hence rumours from previously observed events, which are likely to diverge, need to be leveraged for training. As far as we know, only have tackled stance classification in the context of breaking news applied to new rumours. used Gaussian Processes to perform 3-way stance classification into supporting, denying or questioning, while comments where not considered as part of the task. did include comments to perform 4-way stance classification; they used Hawkes Processes to exploit the temporal sequence of stances towards rumours to classify new tweets discussing rumours. Work by @cite_16 has also performed stance classification for rumours around breaking news, but overlapping rumours were used for training and testing.
@cite_16
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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.