# Looking for COVID-19 misinformation in multilingual social media texts

arXiv: https://arxiv.org/abs/2105.03313 · HTML: https://mlsanity.com/paper/2105.03313/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society](https://mlsanity.com/paper/2005.00033/) (2020)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19](https://mlsanity.com/paper/2006.11343/) (2020)
- [A first look at COVID-19 information and misinformation sharing on Twitter](https://mlsanity.com/paper/2003.13907/) (2020)
- [Causal Modeling of Twitter Activity During COVID-19](https://mlsanity.com/paper/2005.07952/) (2020)
- [Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset](https://mlsanity.com/paper/2003.10359/) (2020)
- [Disinformation and Misinformation on Twitter during the Novel Coronavirus Outbreak](https://mlsanity.com/paper/2006.04278/) (2020)
- [What is the people posting about symptoms related to Coronavirus in Bogota, Colombia?](https://mlsanity.com/paper/2003.11159/) (2020)

## Built on by (most cited first, 1 of 1)

- [Not cool, calm or collected: Using emotional language to detect COVID-19 misinformation](https://mlsanity.com/paper/2303.16777/) (2023)

## 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.