mlsanityData to 7 Oct 2026

TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy

arXiv 2101.03529 ↗First posted 10 Jan 2021cs.SI cs.CLcited by 4

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Abstract (arXiv)

Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. Specifically, the ongoing COVID-19 pandemic has seen a rise of inaccurate and misleading information due to the surrounding controversies and unknown details at the beginning of the pandemic. The FakeNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation based on text and structure from Twitter follower network. In this paper, we present a simple approach that uses BERT embeddings and a shallow neural network for classifying tweets using only text, and discuss our findings and limitations of the approach in text-based misinformation detection.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 22 references that are in the map. First posted on arXiv 10 Jan 2021.

Builds on

11 of 22 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2017Squeeze-and-Excitation Networkscited by 4,763
  4. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  5. 2018A Retrospective Analysis of the Fake News Challenge Stance Detection Taskcited by 49
  6. 2020Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Mediacited by 46
  7. 2017A simple but tough-to-beat baseline for the Fake News Challenge stance detection taskcited by 26
  8. 2018TI-CNN: Convolutional Neural Networks for Fake News Detectioncited by 22
  9. 2018Fake News Identification on Twitter with Hybrid CNN and RNN Modelscited by 17
  10. 2020Accenture at CheckThat! 2020: If you say so: Post-hoc fact-checking of claims using transformer-based modelscited by 15
  11. 2020Check_square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Featurescited by 7

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 1.

4 papers.

  1. 2021On the Role of Images for Analyzing Claims in Social Mediacited by 6
  2. 2022MM-Claims: A Dataset for Multimodal Claim Detection in Social Mediacited by 6
  3. 2021Multimodal Fusion with BERT and Attention Mechanism for Fake News Detectioncited by 5
  4. 2021Generating artificial texts as substitution or complement of training datacited by 2