arXiv:2101.05701

TUDublin team at Constraint@AAAI2021 -- COVID19 Fake News Detection

Elena Shushkevich, John Cardiff

제출 2021-01-14

원문 초록

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.

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arXiv:2506.21002공통 태그 Binary Classification공식 코드 · Papers with Code

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