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.
翻译:本文专门介绍TUDUblin团队在Castraint@AAAI2021-COVID19假新闻探测挑战中的参与情况。今天,假新闻探测问题比以往任何时候更加尖锐。假新闻的数量正在迅速增加,有必要创建AI工具,使我们能够识别和防止关于COVID-19的虚假信息紧急传播。工作的主要目标是建立一个模式,在COVID-19的背景下,将来自社交媒体的信息二进制为真实或假新闻。我们的团队构建了由双向短期内存、支持矢量机器、物流倒退、Naive Bayes以及后勤倒退和Naive Bayes组合组成的共合体。该模式使我们得以实现0.94 F1核心,这在最佳效果的5个范围内。