Stance Detection in the Context of Fake News—A New Approach

Izzat Alsmadi (), Iyad Alazzam, Mohammad Al-Ramahi and Mohammad Zarour ()
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Izzat Alsmadi: Department of Computational, Engineering, and Mathematical Sciences, College of Arts and Science, Texas A&M University, San Antonio, TX 78224, USA
Iyad Alazzam: Department of Information Systems, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid 21163, Jordan
Mohammad Al-Ramahi: Department of Accounting and Finance, College of Business, Texas A&M University, San Antonio, TX 78224, USA
Mohammad Zarour: Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa 13133, Jordan

Future Internet, 2024, vol. 16, issue 10, 1-16

Abstract: Online social networks (OSNs) are inundated with an enormous daily influx of news shared by users worldwide. Information can originate from any OSN user and quickly spread, making the task of fact-checking news both time-consuming and resource-intensive. To address this challenge, researchers are exploring machine learning techniques to automate fake news detection. This paper specifically focuses on detecting the stance of content producers—whether they support or oppose the subject of the content. Our study aims to develop and evaluate advanced text-mining models that leverage pre-trained language models enhanced with meta features derived from headlines and article bodies. We sought to determine whether incorporating the cosine distance feature could improve model prediction accuracy. After analyzing and assessing several previous competition entries, we identified three key tasks for achieving high accuracy: (1) a multi-stage approach that integrates classical and neural network classifiers, (2) the extraction of additional text-based meta features from headline and article body columns, and (3) the utilization of recent pre-trained embeddings and transformer models.

Keywords: stance detection; fake news; data analytics; pre-trained language models; cosine similarity (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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