mlsanityData to 14 Oct 2026

Constraint 2021: Machine Learning Models for COVID-19 Fake News Detection Shared Task

arXiv 2101.03717 ↗First posted 11 Jan 2021cs.CLcited by 5

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Abstract (arXiv)

In this system paper we present our contribution to the Constraint 2021 COVID-19 Fake News Detection Shared Task, which poses the challenge of classifying COVID-19 related social media posts as either fake or real. In our system, we address this challenge by applying classical machine learning algorithms together with several linguistic features, such as n-grams, readability, emotional tone and punctuation. In terms of pre-processing, we experiment with various steps like stop word removal, stemming/lemmatization, link removal and more. We find our best performing system to be based on a linear SVM, which obtains a weighted average F1 score of 95.19% on test data, which lands a place in the middle of the leaderboard (place 80 of 167).

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 7 of its 28 references that are in the map. First posted on arXiv 11 Jan 2021.

Builds on

7 of 28 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2018The Fact Extraction and VERification (FEVER) Shared Taskcited by 108
  3. 2019GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verificationcited by 100
  4. 2020Fighting an Infodemic: COVID-19 Fake News Datasetcited by 83
  5. 2017SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumourscited by 77
  6. 2018RumourEval 2019: Determining Rumour Veracity and Support for Rumourscited by 75
  7. 2018Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthinesscited by 41

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 1; 2025: 0; 2026: 2.

5 papers.