A Lightweight Approach for User and Keyword Classification in Controversial Topics

TL;DR

This paper introduces a lightweight, keyword-based random walk method for classifying user stances and concerns on controversial topics in social media, reducing data requirements and processing time.

Contribution

It presents a novel, minimal-data approach for stance classification in OSNs using only keywords and a tailored random walk model, improving efficiency over existing methods.

Findings

Outperforms baseline methods in stance classification accuracy.

Requires only one keyword per stance, reducing data needs.

Maintains competitive running time despite being not the fastest.

Abstract

Classifying the stance of individuals on controversial topics and uncovering their concerns is crucial for social scientists and policymakers. Data from Online Social Networks (OSNs), which serve as a proxy to a representative sample of society, offers an opportunity to classify these stances, discover society's concerns regarding controversial topics, and track the evolution of these concerns over time. Consequently, stance classification in OSNs has garnered significant attention from researchers. However, most existing methods for this task often rely on labelled data and utilise the text of users' posts or the interactions between users, necessitating large volumes of data, considerable processing time, and access to information that is not readily available (e.g. users' followers/followees). This paper proposes a lightweight approach for the stance classification of users and keywords in OSNs, aiming at understanding the collective opinion of individuals and their concerns. Our approach employs a tailored random walk model, requiring just one keyword representing each stance, using solely the keywords in social media posts. Experimental results demonstrate the superior performance of our method compared to the baselines, excelling in stance classification of users and keywords, with a running time that, while not the fastest, remains competitive.