mlsanityData to 7 Oct 2026

QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection using Multilingual Models

arXiv 2305.03336 ↗First posted 5 May 2023cs.CL cs.AI cs.CYcited by 5

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Abstract (arXiv)

Misinformation spreading in mainstream and social media has been misleading users in different ways. Manual detection and verification efforts by journalists and fact-checkers can no longer cope with the great scale and quick spread of misleading information. This motivated research and industry efforts to develop systems for analyzing and verifying news spreading online. The SemEval-2023 Task 3 is an attempt to address several subtasks under this overarching problem, targeting writing techniques used in news articles to affect readers' opinions. The task addressed three subtasks with six languages, in addition to three ``surprise'' test languages, resulting in 27 different test setups. This paper describes our participating system to this task. Our team is one of the 6 teams that successfully submitted runs for all setups. The official results show that our system is ranked among the top 3 systems for 10 out of the 27 setups.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 19 of its 42 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

19 of 42 references.

References · 19

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7 more
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Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 2.

5 papers.

  1. 2024Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articlescited by 21
  2. 2023ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Textcited by 19
  3. 2023Legend at ArAIEval Shared Task: Persuasion Technique Detection using a Language-Agnostic Text Representation Modelcited by 4
  4. 2024Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Surveycited by 4
  5. 2023Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for Propaganda and Disinformation Detectioncited by 1