mlsanityData to 14 Oct 2026

Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space -- Transformer Ensemble Models Tackling Deception and Persuasion

arXiv 2311.18730 ↗First posted 30 Nov 2023cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

In this paper, we highlight our approach for the "Arabic AI Tasks Evaluation (ArAiEval) Shared Task 2023". We present our approaches for task 1-A and task 2-A of the shared task which focus on persuasion technique detection and disinformation detection respectively. Detection of persuasion techniques and disinformation has become imperative to avoid distortion of authentic information. The tasks use multigenre snippets of tweets and news articles for the given binary classification problem. We experiment with several transformer-based models that were pre-trained on the Arabic language. We fine-tune these state-of-the-art models on the provided dataset. Ensembling is employed to enhance the performance of the systems. We achieved a micro F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task 2-A (7th rank on the leaderboard) respectively.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 30 Nov 2023.

Builds on

8 of 22 references.

References · 8

  1. 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 362
  2. 2020ARBERT & MARBERT: Deep Bidirectional Transformers for Arabiccited by 166
  3. 2017Automatic Detection of Fake Newscited by 145
  4. 2023ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Textcited by 19
  5. 2022Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabiccited by 18
  6. 2018Studying Fake News via Network Analysis: Detection and Mitigationcited by 14
  7. 2019An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Textcited by 9
  8. 2023Detecting and Reasoning of Deleted Tweets before they are Postedcited by 4

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.