Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification. The approach profits from a novel set of automatically identifiable problem-specific features, which significantly boost classifier accuracy and achieve above state-of-the-art results on recent benchmark datasets. This calls into question the value of using complex sophisticated models for stance classification without first doing informed feature extraction.
The first study that tackles automatic stance classification is that of . With a dataset containing 10K tweets and using a Bayesian classifier and three types of features categorised as content'', network'' and Twitter specific memes'', the authors achieved an accuracy of 93.5 use a rule-based method and show that it outperforms the approach reported by . enrich the feature sets investigated by earlier studies by features derived from the Linguistic Inquiry and Word Count (LIWC) dictionaries @cite_7 . investigate Gaussian Processes as rumour stance classifier. For the first time the authors also use Brown Clusters to extract the features for each tweet. Unlike researchers above, evalute on the rumour data released by , where they report an accuracy of 67.7 Subsequent work has also tackled stance classification for new, unseen rumours. @cite_23 moved away from the classification of tweets in isolation, focusing instead on Twitter 'conversations' @cite_25 initiated by rumours, as part of the Pheme project @cite_13 . They looked at tree-structured conversations initiated by a rumour and followed by tweets responding to it by supporting, denying, querying or commenting on the rumour.
@cite_13 @cite_23 @cite_25 @cite_7
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PHEME attempts to identify four kinds of false claim in social media and on the web, in real time: rumours, disinformation, misinformation and speculation. This brings challenges in modelling the behaviour of individual users, networks of users and information diffusion. This presentation proposal discusses the issues addressed by the project and the challenges it faces, in this emerging and rapidly-developing domain. Inspired by a European project, PHEME, that requires the close analysis of Twitter-based conversations in order to look at the spread of rumors via social media, this paper has two objectives. The first of these is to take the analysis of microblogs back to first principles and lay out what microblog analysis should look like as a foundational programme of work. The other is to describe how this is of fundamental relevance to Human-Computer Interaction's interest in grasping the constitution of people's interactions with technology within the social order. Our critical finding is that, despite some surface similarities, Twitter-based conversations are a wholly distinct social phenomenon requiring an independent analysis that treats them as unique phenomena in their own right, rather than as another species of conversation that can be handled within the framework of existing Conversation Analysis. This motivates the argument that Microblog Analysis be established as a foundationally independent programme, examining the organizational characteristics of microblogging from the ground up. We articulate how aspects of this approach have already begun to shape our design activities within the PHEME project. We are in the midst of a technological revolution whereby, for the first time, researchers can link daily word use to a broad array of real-world behaviors. This article reviews several computerized text analysis methods and describes how Linguistic Inquiry and Word Count (LIWC) was created and validated. LIWC is a transparent text analysis program that counts words in psychologically meaningful categories. Empirical results using LIWC demonstrate its ability to detect meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles, and individual differences.