A Semi-Supervised Approach to Message Stance Classification

Abstract

Social media communications are becoming increasingly prevalent; some useful, some false, whether unwittingly or maliciously. An increasing number of rumours daily flood the social networks. Determining their veracity in an autonomous way is a very active and challenging field of research, with a variety of methods proposed. However, most of the models rely on determining the constituent messages' stance towards the rumour, a feature known as the "wisdom of the crowd". Although several supervised machine-learning approaches have been proposed to tackle the message stance classification problem, these have numerous shortcomings. In this paper we argue that semi-supervised learning is more effective than supervised models and use two graph-based methods to demonstrate it. This is not only in terms of classification accuracy, but equally important, in terms of speed and scalability. We use the Label Propagation and Label Spreading algorithms and run experiments on a dataset of 72 rumours and hundreds of thousands messages collected from Twitter. We compare our results on two available datasets to the state-of-theart to demonstrate our algorithms' performance regarding accuracy, speed and scalability for real-time applications.

Stance Detection

“…Rumour stance detection is a recent and popular research topic, and therefore, rumour classification is an important application area of stance detection. Similar to the approaches for stance detection, those for rumour stance detection are usually supervised machine learning approaches with different feature sets [Lukasik et al 2019;Pamungkas et al 2019;Zubiaga et al 2018aZubiaga et al , 2016Zubiaga et al , 2018b in addition to semi-supervised approaches [Giasemidis et al 2018]. Approaches based on deep learning methods [Zubiaga et al 2018b] and those additionally utilizing attention mechanisms [Veyseh et al 2017] are employed for rumour stance detection as well.…”

Beyond facts – a survey and conceptualisation of claims in online discourse analysis

“…They can be expressed explicitly or implicitly [146]. Fragments can be messages such as tweets or posts [55,86], paragraphs [144] or complete articles [70]. Joseph et al [86] see stances as latent properties of users rather than text fragments.…”

Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations

“…These studies can be roughly categorized into two groups. One line of work aims to design different features to capture the sequential property of conversation threads Aker et al, 2017;Pamungkas et al, 2018;Zubiaga et al, 2018b;Giasemidis et al, 2018). Another line of work attempts to apply recent deep learning models to automatically capture effective stance features (Kochkina et al, 2017;Veyseh et al, 2017).…”