A multi-faceted NLP analysis of misinformation spreaders in Twitter

Antypas, Dimosthenis, Preece, Alun ORCID: https://orcid.org/0000-0003-0349-9057 and Camacho Collados, Jose ORCID: https://orcid.org/0000-0003-1618-7239 2024. A multi-faceted NLP analysis of misinformation spreaders in Twitter. Presented at: 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, Bangkok, Thailand, 15 August 2024. Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis. Association for Computational Linguistics, pp. 71-83.

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Abstract

Social media is an integral part of the daily life of an increasingly large number of people worldwide. Used for entertainment, communication and news updates, it constitutes a source of information that has been extensively used to study human behaviour. Unfortunately, the open nature of social media platforms along with the difficult task of supervising their content has led to a proliferation of misinformation posts. In this paper, we aim to identify the textual differences between the profiles of user that share misinformation from questionable sources and those that do not. Our goal is to better understand user behaviour in order to be better equipped to combat this issue. To this end, we identify Twitter (X) accounts of potential misinformation spreaders and apply transformer models specialised in social media to extract characteristics such as sentiment, emotion, topic and presence of hate speech. Our results indicate that, while there may be some differences between the behaviour of users that share misinformation and those that do not, there are no large differences when it comes to the type of content shared.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computational Linguistics
ISBN:	979-8-89176-156-8
Date of First Compliant Deposit:	10 October 2024
Date of Acceptance:	1 June 2024
Last Modified:	14 Oct 2024 13:45
URI:	https://orca.cardiff.ac.uk/id/eprint/172836

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