Matching tweets with applicable fact-checks across languages
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 post...
| Main Authors: | , , , , |
|---|---|
| Format: | Conference item |
| Language: | English |
| Published: |
CEUR Workshop Proceedings
2022
|
| _version_ | 1797109638031736832 |
|---|---|
| author | Kazemi, A Li, Z Peréz-Rosas, V Hale, SA Mihalcea, R |
| author_facet | Kazemi, A Li, Z Peréz-Rosas, V Hale, SA Mihalcea, R |
| author_sort | Kazemi, A |
| collection | OXFORD |
| description | 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 (86% average accuracy) in four language pairs. We also find that a BM25 baseline outperforms or is on par with 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. |
| first_indexed | 2024-03-07T07:42:52Z |
| format | Conference item |
| id | oxford-uuid:b17db0f6-169b-4f09-94ce-6913c4cf3cf9 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:42:52Z |
| publishDate | 2022 |
| publisher | CEUR Workshop Proceedings |
| record_format | dspace |
| spelling | oxford-uuid:b17db0f6-169b-4f09-94ce-6913c4cf3cf92023-05-12T10:34:10ZMatching tweets with applicable fact-checks across languagesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:b17db0f6-169b-4f09-94ce-6913c4cf3cf9EnglishSymplectic ElementsCEUR Workshop Proceedings2022Kazemi, ALi, ZPeréz-Rosas, VHale, SAMihalcea, RAn 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 (86% average accuracy) in four language pairs. We also find that a BM25 baseline outperforms or is on par with 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. |
| spellingShingle | Kazemi, A Li, Z Peréz-Rosas, V Hale, SA Mihalcea, R Matching tweets with applicable fact-checks across languages |
| title | Matching tweets with applicable fact-checks across languages |
| title_full | Matching tweets with applicable fact-checks across languages |
| title_fullStr | Matching tweets with applicable fact-checks across languages |
| title_full_unstemmed | Matching tweets with applicable fact-checks across languages |
| title_short | Matching tweets with applicable fact-checks across languages |
| title_sort | matching tweets with applicable fact checks across languages |
| work_keys_str_mv | AT kazemia matchingtweetswithapplicablefactchecksacrosslanguages AT liz matchingtweetswithapplicablefactchecksacrosslanguages AT perezrosasv matchingtweetswithapplicablefactchecksacrosslanguages AT halesa matchingtweetswithapplicablefactchecksacrosslanguages AT mihalcear matchingtweetswithapplicablefactchecksacrosslanguages |