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

arXiv: https://arxiv.org/abs/2311.18730 · HTML: https://mlsanity.com/paper/2311.18730/

## Key facts

- Cited by 1 papers 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 (most cited first, 8 of 8)

- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text](https://mlsanity.com/paper/2311.03179/) (2023)
- [Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic](https://mlsanity.com/paper/2211.10057/) (2022)
- [Studying Fake News via Network Analysis: Detection and Mitigation](https://mlsanity.com/paper/1804.10233/) (2018)
- [An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text](https://mlsanity.com/paper/1912.06745/) (2019)
- [Detecting and Reasoning of Deleted Tweets before they are Posted](https://mlsanity.com/paper/2305.04927/) (2023)

## Built on by (most cited first, 1 of 1)

- [FABRIC: Framework for Agent-Based Realistic Intelligence Creation](https://mlsanity.com/paper/2510.17995/) (2025)

## 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.