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Social Network Analysis and Mining

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01.12.2021 | Original Article

Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization

verfasst von: Thirunavukarasu Balasubramaniam, Richi Nayak, Khanh Luong, Md. Abul Bashar

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2021

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Abstract

Social media platforms like Twitter have become an easy portal for billions of people to connect and exchange their thoughts. Unfortunately, people commonly use these platforms to share misinformation which can influence other people adversely. The spread of misinformation is unavoidable in an extraordinary situation like Covid-19, and the consequences can be dreadful. This paper proposes a two-step ranking-based misinformation detection (RMiD) technique. Firstly, a novel ranking-based approach leveraging the scalable information retrieval infrastructure is applied to detect misinformation from a huge collection of unlabelled tweets based on a related but very small labelled misinformation data set. Secondly, the identified misinformation tweets are represented as a coupled matrix tensor model and Nonnegative Coupled Matrix Tensor Factorization is applied to learn their spatio-temporal topic dynamics. The experimental analysis shows that RMiD is capable of detecting misinformation with better coverage and less noise in comparison with existing techniques. Moreover, the coupled matrix tensor representation has improved the quality of topics discovered from unlabelled data up to 4% by leveraging the semantic similarity of terms in labelled data.

Vorheriger Artikel The nature, cause and consequence of COVID-19 panic among social media users in India

Nächster Artikel CHECKED: Chinese COVID-19 fake news dataset

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Titel: Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization
verfasst von: Thirunavukarasu Balasubramaniam
Richi Nayak
Khanh Luong
Md. Abul Bashar
Publikationsdatum: 01.12.2021
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2021
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-021-00767-7

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