@inproceedings{dougrez-lewis-etal-2024-knowledge,
title = "Knowledge Graphs for Real-World Rumour Verification",
author = "Dougrez-Lewis, John and
Kochkina, Elena and
Liakata, Maria and
He, Yulan",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.860",
pages = "9843--9853",
abstract = "Despite recent progress in automated rumour verification, little has been done on evaluating rumours in a real-world setting. We advance the state-of-the-art on the PHEME dataset, which consists of Twitter response threads collected as a rumour was unfolding. We automatically collect evidence relevant to PHEME and use it to construct knowledge graphs in a time-sensitive manner, excluding information post-dating rumour emergence. We identify discrepancies between the evidence retrieved and PHEME{'}s labels, which are discussed in detail and amended to release an updated dataset. We develop a novel knowledge graph approach which finds paths linking disjoint fragments of evidence. Our rumour verification model which combines evidence from the graph outperforms the state-of-the-art on PHEME and has superior generisability when evaluated on a temporally distant rumour verification dataset.",
}
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<abstract>Despite recent progress in automated rumour verification, little has been done on evaluating rumours in a real-world setting. We advance the state-of-the-art on the PHEME dataset, which consists of Twitter response threads collected as a rumour was unfolding. We automatically collect evidence relevant to PHEME and use it to construct knowledge graphs in a time-sensitive manner, excluding information post-dating rumour emergence. We identify discrepancies between the evidence retrieved and PHEME’s labels, which are discussed in detail and amended to release an updated dataset. We develop a novel knowledge graph approach which finds paths linking disjoint fragments of evidence. Our rumour verification model which combines evidence from the graph outperforms the state-of-the-art on PHEME and has superior generisability when evaluated on a temporally distant rumour verification dataset.</abstract>
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%0 Conference Proceedings
%T Knowledge Graphs for Real-World Rumour Verification
%A Dougrez-Lewis, John
%A Kochkina, Elena
%A Liakata, Maria
%A He, Yulan
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F dougrez-lewis-etal-2024-knowledge
%X Despite recent progress in automated rumour verification, little has been done on evaluating rumours in a real-world setting. We advance the state-of-the-art on the PHEME dataset, which consists of Twitter response threads collected as a rumour was unfolding. We automatically collect evidence relevant to PHEME and use it to construct knowledge graphs in a time-sensitive manner, excluding information post-dating rumour emergence. We identify discrepancies between the evidence retrieved and PHEME’s labels, which are discussed in detail and amended to release an updated dataset. We develop a novel knowledge graph approach which finds paths linking disjoint fragments of evidence. Our rumour verification model which combines evidence from the graph outperforms the state-of-the-art on PHEME and has superior generisability when evaluated on a temporally distant rumour verification dataset.
%U https://aclanthology.org/2024.lrec-main.860
%P 9843-9853
Markdown (Informal)
[Knowledge Graphs for Real-World Rumour Verification](https://aclanthology.org/2024.lrec-main.860) (Dougrez-Lewis et al., LREC-COLING 2024)
ACL
- John Dougrez-Lewis, Elena Kochkina, Maria Liakata, and Yulan He. 2024. Knowledge Graphs for Real-World Rumour Verification. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9843–9853, Torino, Italia. ELRA and ICCL.