Looking for COVID-19 misinformation in multilingual social media texts
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Abstract (arXiv)
This paper presents the Multilingual COVID-19 Analysis Method (CMTA) for detecting and observing the spread of misinformation about this disease within texts. CMTA proposes a data science (DS) pipeline that applies machine learning models for processing, classifying (Dense-CNN) and analyzing (MBERT) multilingual (micro)-texts. DS pipeline data preparation tasks extract features from multilingual textual data and categorize it into specific information classes (i.e., 'false', 'partly false', 'misleading'). The CMTA pipeline has been experimented with multilingual micro-texts (tweets), showing misinformation spread across different languages. To assess the performance of CMTA and put it in perspective, we performed a comparative analysis of CMTA with eight monolingual models used for detecting misinformation. The comparison shows that CMTA has surpassed various monolingual models and suggests that it can be used as a general method for detecting misinformation in multilingual micro-texts. CMTA experimental results show misinformation trends about COVID-19 in different languages during the first pandemic months.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 3 May 2021.
Builds on
10 of 27 references.
References · 10
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- 2020A first look at COVID-19 information and misinformation sharing on Twittercited by 13
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- 2020Understanding the perception of COVID-19 policies by mining a multilanguage Twitter datasetcited by 4
- 2020Disinformation and Misinformation on Twitter during the Novel Coronavirus Outbreakcited by 3
- 2020What is the people posting about symptoms related to Coronavirus in Bogota, Colombia?cited by 1
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