Rumour Detection via Zero-shot Cross-lingual Transfer Learning

TL;DR

This paper introduces a zero-shot cross-lingual transfer learning approach for rumour detection on social media, enabling models trained in one language to adapt to others without extensive annotated data.

Contribution

The paper presents a novel framework combining pretrained multilingual models and self-training to transfer rumour detection capabilities across languages.

Findings

Outperforms benchmarks in English rumour detection

Effective in Chinese rumour detection

Requires no annotated data in target language

Abstract

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.