mlsanityData to 14 Oct 2026

Rumour Detection via Zero-shot Cross-lingual Transfer Learning

arXiv 2109.12773 ↗First posted 27 Sep 2021cs.CLcited by 5

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Abstract (arXiv)

Most rumour detection models for social media are designed for one specific language (mostly English). There are over 40 languages on Twitter and most languages lack annotated resources to build rumour detection models. In this paper we propose a zero-shot cross-lingual transfer learning framework that can adapt a rumour detection model trained for a source language to another target language. Our framework utilises pretrained multilingual language models (e.g. multilingual BERT) and a self-training loop to iteratively bootstrap the creation of ''silver labels'' in the target language to adapt the model from the source language to the target language. We evaluate our methodology on English and Chinese rumour datasets and demonstrate that our model substantially outperforms competitive benchmarks in both source and target language rumour detection.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 21 of its 41 references that are in the map. First posted on arXiv 27 Sep 2021.

Builds on

21 of 41 references.

References · 21

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9 more
  1. 2016Quasi-Recurrent Neural Networkscited by 87
  2. 2018All-in-one: Multi-task Learning for Rumour Verificationcited by 54
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 1.

5 papers.

  1. 2024A Survey on Multilingual Large Language Models: Corpora, Alignment, and Biascited by 84
  2. 2022Zero-Shot Rumor Detection with Propagation Structure via Prompt Learningcited by 32
  3. 2023Fine-Tuning BERT with Character-Level Noise for Zero-Shot Transfer to Dialects and Closely-Related Languagescited by 9
  4. 2025Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagementcited by 6
  5. 2024Rumour Evaluation with Very Large Language Modelscited by 0