Misinformation in Social Media Platforms and Web Articles: a Dataset to Infer User Stance

Social media is filled with news articles, interesting stories and entertainment news. It is a given that along with most of these legitimate stories and articles some of them may also be misinformation. The problem of detecting misinformation and identifying whether an article title misleads reader...

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Bibliographic Details
Published in:2022 IEEE 16th International Conference on Semantic Computing (ICSC) pp. 269 - 273
Main Authors: Abeysinghe, Bhashithe, Vulupala, Gyandeep Reddy, Sunderraman, Rajshekhar
Format: Conference Proceeding
Language:English
Published: IEEE 01.01.2022
Subjects:
Blogs
Conferences
Entertainment industry
Fake news
misinformation
Semantics
social media
Social networking (online)
Twitter dataset
user stance
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Summary:Social media is filled with news articles, interesting stories and entertainment news. It is a given that along with most of these legitimate stories and articles some of them may also be misinformation. The problem of detecting misinformation and identifying whether an article title misleads readers has been studied. However, work related to user's stance of a shared article has not been explored much. This paper tries to fill that gap by introducing a public dataset which includes users text contribution, article title, article content and a link to the article.
DOI:10.1109/ICSC52841.2022.00051