mlsanityData to 7 Oct 2026

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

arXiv 2101.05701 ↗First posted 14 Jan 2021cs.CLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 23 references that are in the map. First posted on arXiv 14 Jan 2021.

Builds on

4 of 23 references.

References · 4

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2020Fighting an Infodemic: COVID-19 Fake News Datasetcited by 82
  3. 2016Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Poolingcited by 39
  4. 2020COVID-19 Pandemic: Identifying Key Issues using Social Media and Natural Language Processingcited by 2

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Citing papers per year: 2021: 2; 2022: 1.

3 papers.