TUDublin team at Constraint@AAAI2021 -- COVID19 Fake News Detection
This work addresses the urgent need to identify and prevent the spread of false information about COVID-19, though it is incremental as it builds on existing methods for a specific challenge.
The paper tackled COVID-19 fake news detection by developing an ensemble model for binary classification of social media messages, achieving an F1-score of 0.94, which is within 5% of the best result in the challenge.
The paper is devoted to the participation of the TUDublin team in Constraint@AAAI2021 - COVID19 Fake News Detection Challenge. Today, the problem of fake news detection is more acute than ever in connection with the pandemic. The number of fake news is increasing rapidly and it is necessary to create AI tools that allow us to identify and prevent the spread of false information about COVID-19 urgently. The main goal of the work was to create a model that would carry out a binary classification of messages from social media as real or fake news in the context of COVID-19. Our team constructed the ensemble consisting of Bidirectional Long Short Term Memory, Support Vector Machine, Logistic Regression, Naive Bayes and a combination of Logistic Regression and Naive Bayes. The model allowed us to achieve 0.94 F1-score, which is within 5\% of the best result.