Looking for COVID-19 misinformation in multilingual social media texts
This addresses the spread of misinformation during the COVID-19 pandemic for public health and social media monitoring, but it is incremental as it builds on existing multilingual and misinformation detection methods.
The paper tackles the problem of detecting COVID-19 misinformation in multilingual social media texts by proposing the CMTA pipeline, which uses machine learning models like Dense-CNN and MBERT to classify texts into categories such as 'false' or 'misleading', and it shows that CMTA surpasses eight monolingual models in performance.
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.