Multi-Task Learning Framework for Stance Detection and Veracity Prediction

Tracking #: 2827-4041

This paper is currently under review
Fatima T. Alkhawaldeh
Tommy Yuan
Dimitar Kazakov1

Responsible editor: 
Maria Maleshkova

Submission type: 
Full Paper
As more people rely on online media, it becomes more challenging to identify trustworthy information. As a result of this increased complexity, stance detection and rumour detection have gained prominence. Although both tasks are highly correlated and should be performed concurrently, most existing models train them independently. Additionally, while each target topic may contain numerous conflicting claims, previous work treated each claim independently, resulting in conflict claims wrongly assigned with the same truth label. Because some lengthy rumour posts cover a wide range of topics, determining the positions of the posts can be done with a variety of target topics. Existing models may take a biased position toward the correct target topic or the incorrect target topic, resulting in an incorrect determination of veracity. The purpose of this article is to address these problems by proposing a framework for stance detection and veracity prediction that takes into account source credibility and compares the strength of arguments in order to forecast the truth. Experiments are conducted using two well-known datasets: Emergent and RumourEval-2019. On the gold-standard datasets, the results demonstrate that the proposed framework outperforms other methods
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