Claim Extraction and Dynamic Stance Detection in COVID-19 Tweets
ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023
; : 1059-1068, 2023.
Article
in English
| Scopus | ID: covidwho-20242328
ABSTRACT
The information ecosystem today is noisy, and rife with messages that contain a mix of objective claims and subjective remarks or reactions. Any automated system that intends to capture the social, cultural, or political zeitgeist, must be able to analyze the claims as well as the remarks. Due to the deluge of such messages on social media, and their tremendous power to shape our perceptions, there has never been a greater need to automate these analyses, which play a pivotal role in fact-checking, opinion mining, understanding opinion trends, and other such downstream tasks of social consequence. In this noisy ecosystem, not all claims are worth checking for veracity. Such a check-worthy claim, moreover, must be accurately distilled from subjective remarks surrounding it. Finally, and especially for understanding opinion trends, it is important to understand the stance of the remarks or reactions towards that specific claim. To this end, we introduce a COVID-19 Twitter dataset, and present a three-stage process to (i) determine whether a given Tweet is indeed check-worthy, and if so, (ii) which portion of the Tweet ought to be checked for veracity, and finally, (iii) determine the author's stance towards the claim in that Tweet, thus introducing the novel task of topic-agnostic stance detection. © 2023 ACM.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023
Year:
2023
Document Type:
Article
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