Looking for COVID-19 misinformation in multilingual social media texts

Raj Ratn-Pranesh (1) , Mehrdad Farokhnejad (2) , Ambesh Shekhar (1) , Genoveva Vargas-Solar (3)

1 BIT Mesra - Birla Institute of Technology [Mesra]
2 HADAS - Heterogeneous and Adaptive distributed DAta management Systems
3 BD - Base de Données

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.

Mots clés: data science; data exploration; misinformation; machine learning; natural language processing

Domaines: Base de données [cs.DB]

Raj Ratn-Pranesh, Mehrdad Farokhnejad, Ambesh Shekhar, Genoveva Vargas-Solar. Looking for COVID-19 misinformation in multilingual social media texts. 25th European Conference on Advances in Databases and Information Systems, University of Tartu, Aug 2021, Tartu, Estonia. ⟨10.1007/978-3-030-85082-1_7⟩. ⟨hal-03314988⟩