Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2023

Abstract

In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public. Rumors can trigger versatile, mostly controversial stance expressions among social media users. Rumor verification and stance detection are different yet relevant tasks. Fake news debunking primarily focuses on determining the truthfulness of news articles, which oversimplifies the issue as fake news often combines elements of both truth and falsehood. Thus, it becomes crucial to identify specific instances of misinformation within the articles. In this research, we investigate a novel task in the field of fake news debunking, which involves detecting sentence-level misinformation. One of the major challenges in this task is the absence of a training dataset with sentence-level annotations regarding veracity. Inspired by the Multiple Instance Learning (MIL) approach, we propose a model called Weakly Supervised Detection of Misinforming Sentences (WSDMS). This model only requires bag-level labels for training but is capable of inferring both sentence-level misinformation and article-level veracity, aided by relevant social media conversations that are attentively contextualized with news sentences. We evaluate WSDMS on three real-world benchmarks and demonstrate that it outperforms existing state-of-the-art baselines in debunking fake news at both the sentence and article levels.

Keywords

fake news debunking, truthfulness, misinformation, sentence-level detection, research, Multiple Instance Learning (MIL), Weakly Supervised Detection of Misinforming Sentences (WSDMS), bag-level labels, article-level veracity, social media conversations, benchmarks, state-of-the-art baselines, debunking, sentence and article levels

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Programming Languages and Compilers

Research Areas

Data Science and Engineering

Publication

2023 Conference on Empirical Methods in Natural Language Processing: Singapore, December 6-10: Proceedings

First Page

1525

Last Page

1538

ISBN

9798891760608

Identifier

10.18653/v1/2023.emnlp-main.94

Publisher

Association for Computational Linguistics

City or Country

Stroudsburg, PA

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.18653/v1/2023.emnlp-main.94

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