This paper describes the Nexus team's participation in the ArAIEval shared task focused on detecting propaganda and disinformation in Arabic. The team fine-tuned transformer models and experimented with zero- and few-shot learning using GPT-4. Nexus's system achieved 9th place in subtask 1A and 10th place in subtask 2A. Why it matters: The work contributes to the important goal of automatically identifying and mitigating the spread of disinformation in Arabic content, which is critical for maintaining societal trust and informed public discourse.
The paper introduces MultiProSE, the first multi-label Arabic dataset for propaganda, sentiment, and emotion detection. It extends the existing ArPro dataset with sentiment and emotion annotations, resulting in 8,000 annotated news articles. Baseline models, including GPT-4o-mini and BERT-based models, were developed for each task, and the dataset, guidelines, and code are publicly available. Why it matters: This resource enables further research into Arabic language models and a better understanding of opinion dynamics within Arabic news media.
This paper introduces a new task: detecting propaganda techniques in code-switched text. The authors created and released a corpus of 1,030 English-Roman Urdu code-switched texts annotated with 20 propaganda techniques. Experiments show the importance of directly modeling multilinguality and using the right fine-tuning strategy for this task.
This paper provides an overview of the UrduFake@FIRE2021 shared task, which focused on fake news detection in the Urdu language. The task involved binary classification of news articles into real or fake categories using a dataset of 1300 training and 300 testing articles across five domains. 34 teams registered, with 18 submitting results and 11 providing technical reports detailing various approaches from BoW to Transformer models, with the best system achieving an F1-macro score of 0.679.
The paper introduces AraELECTRA, a new Arabic language representation model. AraELECTRA is pre-trained using the replaced token detection objective on large Arabic text corpora. The model is evaluated on multiple Arabic NLP tasks, including reading comprehension, sentiment analysis, and named-entity recognition. Why it matters: AraELECTRA outperforms current state-of-the-art Arabic language representation models, given the same pretraining data and even with a smaller model size, advancing Arabic NLP.