Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

Abstract

To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a mixture of signals that might reflect user's beliefs including posts and online interactions. This paper examines various online features of users to detect their stance towards different topics. We compare multiple set of features, including on-topic content, network interactions, user's preferences, and online network connections. Our objective is to understand the online signals that can reveal the users' stance. Experimentation is applied on tweets dataset from the SemEval stance detection task, which covers five topics. Results show that stance of a user can be detected with multiple signals of user's online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like. The performance of the stance modelling using different network features are comparable with the state-of-the-art reported model that used textual content only. In addition, combining network and content features leads to the highest reported performance to date on the SemEval dataset with F-measure of 72.49%.We further present an extensive analysis to show how these different set of features can reveal stance. Our findings have distinct privacy implications, where they highlight that stance is strongly embedded in user's online social network that, in principle, individuals can be profiled from their interactions and connections even when they do not post about the topic.

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

“…Abeer Aldayel and Walid Magdy [31] (2019) categorized Twitter data into likes, interaction (mention, retweet, and replies), connection (follow) and textual contents. The stance was analyzed using linear SVM which offered good results.…”

Will-They-Won’t-They: A Very Large Dataset for Stance Detection on Twitter

“…Our experiments with 11 strong models indicated a consistent (>10%) performance gap between the state-of-the-art and human upperbound, which proves that WT-WT constitutes a strong challenge for current models. Future research directions might explore the usage of transformer-based models, as well as of models which exploit not only linguistic but also network features, which have been proven to work well for existing stance detection datasets (Aldayel and Magdy, 2019). Also, the multi-domain nature of the dataset enables future research in cross-target and crossdomain adaptation, a clear weak point of current models according to our evaluations.…”

Knowledge Enhanced Masked Language Model for Stance Detection

“…The most well-known data for political stance detection is published by the SemEval 2016 (Mohammad et al, 2016b;Aldayel and Magdy, 2019). The paper describing the data set provides a highlevel review of approaches to stance detection using Twitter data.…”