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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.</abstract>
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%0 Conference Proceedings
%T A Multi-Faceted NLP Analysis of Misinformation Spreaders in Twitter
%A Antypas, Dimosthenis
%A Preece, Alun
%A Camacho-Collados, Jose
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Barnes, Jeremy
%Y Klinger, Roman
%Y Sedoc, João
%Y Tafreshi, Shabnam
%S Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F antypas-etal-2024-multi
%X 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.
%U https://aclanthology.org/2024.wassa-1.7
%P 71-83

Markdown (Informal)

[A Multi-Faceted NLP Analysis of Misinformation Spreaders in Twitter](https://aclanthology.org/2024.wassa-1.7) (Antypas et al., WASSA-WS 2024)

• A Multi-Faceted NLP Analysis of Misinformation Spreaders in Twitter (Antypas et al., WASSA-WS 2024)

ACL

• Dimosthenis Antypas, Alun Preece, and Jose Camacho-Collados. 2024. A Multi-Faceted NLP Analysis of Misinformation Spreaders in Twitter. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 71–83, Bangkok, Thailand. Association for Computational Linguistics.