2021/09/27 by Lin Tian, Tian, Lin, Xiuzhen Zhang +3
Computer Science · Physics and Astronomy · Social Sciences · #Artificial intelligence #Complex Network Analysis Techniques #Computation and Language (cs.CL) #Computer science #FOS: Computer and information sciences #Language model #Linguistics #Misinformation and Its Impacts #Natural language processing #Shot (pellet) #Social media #Topic Modeling #Transfer of learning #World Wide Web #Zero (linguistics)
paper · pdf · doi:10.48550/arxiv.2109.12773
openalex publication_date 2021/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.