Matching Tweets With Applicable Fact-Checks Across Languages

• URL: http://arxiv.org/abs/2202.07094v1
• Date: Mon, 14 Feb 2022 23:33:02 GMT
• Title: Matching Tweets With Applicable Fact-Checks Across Languages
• Authors: Ashkan Kazemi, Zehua Li, Ver\'onica P\'erez-Rosas, Scott A. Hale, Rada Mihalcea
• Abstract summary: We focus on automatically finding existing fact-checks for claims made in social media posts (tweets) We conduct both classification and retrieval experiments, in monolingual (English only), multilingual (Spanish, Portuguese), and cross-lingual (Hindi-English) settings. We present promising results for "match" classification (93% average accuracy) in four language pairs.
• Score: 27.762055254009017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An important challenge for news fact-checking is the effective dissemination of existing fact-checks. This in turn brings the need for reliable methods to detect previously fact-checked claims. In this paper, we focus on automatically finding existing fact-checks for claims made in social media posts (tweets). We conduct both classification and retrieval experiments, in monolingual (English only), multilingual (Spanish, Portuguese), and cross-lingual (Hindi-English) settings using multilingual transformer models such as XLM-RoBERTa and multilingual embeddings such as LaBSE and SBERT. We present promising results for "match" classification (93% average accuracy) in four language pairs. We also find that a BM25 baseline outperforms state-of-the-art multilingual embedding models for the retrieval task during our monolingual experiments. We highlight and discuss NLP challenges while addressing this problem in different languages, and we introduce a novel curated dataset of fact-checks and corresponding tweets for future research.

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