mlsanityData to 14 Oct 2026

Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey

arXiv 2410.18390 ↗First posted 24 Oct 2024cs.CLcited by 4

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Abstract (arXiv)

In today's global digital landscape, misinformation transcends linguistic boundaries, posing a significant challenge for moderation systems. Most approaches to misinformation detection are monolingual, focused on high-resource languages, i.e., a handful of world languages that have benefited from substantial research investment. This survey provides a comprehensive overview of the current research on misinformation detection in low-resource languages, both in monolingual and multilingual settings. We review existing datasets, methodologies, and tools used in these domains, identifying key challenges related to: data resources, model development, cultural and linguistic context, and real-world applications. We examine emerging approaches, such as language-generalizable models and multi-modal techniques, and emphasize the need for improved data collection practices, interdisciplinary collaboration, and stronger incentives for socially responsible AI research. Our findings underscore the importance of systems capable of addressing misinformation across diverse linguistic and cultural contexts.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 43 of its 162 references that are in the map. First posted on arXiv 24 Oct 2024.

Builds on

43 of 162 references.

References · 43

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2023GPT-4 Technical Reportcited by 18,396
  5. 2024The Llama 3 Herd of Modelscited by 14,715
  6. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  7. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  8. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  9. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  10. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 798
  11. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
  12. 2023MEGA: Multilingual Evaluation of Generative AIcited by 293
31 more
  1. 2021MuRIL: Multilingual Representations for Indian Languagescited by 179
  2. 2023Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Promptingcited by 165
  3. 2020Directions in Abusive Language Training Data: Garbage In, Garbage Outcited by 140
  4. 2021BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Banglacited by 125
  5. 2020IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understandingcited by 109
  6. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  7. 2023The science of fake newscited by 97
  8. 2020Low-resource Languages: A Review of Past Work and Future Challengescited by 91
  9. 2021X-FACT: A New Benchmark Dataset for Multilingual Fact Checkingcited by 91
  10. 2021A Survey on Stance Detection for Mis- and Disinformation Identificationcited by 75
  11. 2018Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processingcited by 63
  12. 2020FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19cited by 61
  13. 2022MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Datasetcited by 61
  14. 2023Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Reviewcited by 50
  15. 2020BanFakeNews: A Dataset for Detecting Fake News in Banglacited by 40
  16. 2019Improving Pre-Trained Multilingual Models with Vocabulary Expansioncited by 28
  17. 2023Emotion Detection for Misinformation: A Reviewcited by 22
  18. 2021AI4D -- African Language Programcited by 18
  19. 2022Matching Tweets With Applicable Fact-Checks Across Languagescited by 15
  20. 2022Do Multilingual Language Models Capture Differing Moral Norms?cited by 15
  21. 2021Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detectioncited by 14
  22. 2020No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detectioncited by 13
  23. 2019Evaluating Language Model Finetuning Techniques for Low-resource Languagescited by 12
  24. 2019The Future of Misinformation Detection: New Perspectives and Trendscited by 10
  25. 2023MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapterscited by 7
  26. 2021Designing Language Technologies for Social Good: The Road not Takencited by 5
  27. 2023QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection using Multilingual Modelscited by 5
  28. 2022Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021cited by 4
  29. 2023Distributional Data Augmentation Methods for Low Resource Languagecited by 4
  30. 2023BanMANI: A Dataset to Identify Manipulated Social Media News in Banglacited by 3
  31. 2022Z-Index at CheckThat! Lab 2022: Check-Worthiness Identification on Tweet Textcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025When Scale Meets Diversity: Evaluating Language Models on Fine-Grained Multilingual Claim Verificationcited by 3
  2. 2026BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languagescited by 2
  3. 2025The Hardness of Achieving Impact in AI for Social Impact Research: A Ground-Level View of Challenges & Opportunitiescited by 1
  4. 2025NaijaNLP: A Survey of Nigerian Low-Resource Languagescited by 0