@inproceedings{dougrez-lewis-etal-2022-phemeplus,
title = "{PHEMEP}lus: Enriching Social Media Rumour Verification with External Evidence",
author = "Dougrez-Lewis, John and
Kochkina, Elena and
Arana-Catania, Miguel and
Liakata, Maria and
He, Yulan",
editor = "Aly, Rami and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Guo, Zhijiang and
Mittal, Arpit and
Schlichtkrull, Michael and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.fever-1.6",
doi = "10.18653/v1/2022.fever-1.6",
pages = "49--58",
abstract = "Work on social media rumour verification utilises signals from posts, their propagation and users involved. Other lines of work target identifying and fact-checking claims based on information from Wikipedia, or trustworthy news articles without considering social media context. However works combining the information from social media with external evidence from the wider web are lacking. To facilitate research in this direction, we release a novel dataset, PHEMEPlus, an extension of the PHEME benchmark, which contains social media conversations as well as relevant external evidence for each rumour. We demonstrate the effectiveness of incorporating such evidence in improving rumour verification models. Additionally, as part of the evidence collection, we evaluate various ways of query formulation to identify the most effective method.",
}
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<abstract>Work on social media rumour verification utilises signals from posts, their propagation and users involved. Other lines of work target identifying and fact-checking claims based on information from Wikipedia, or trustworthy news articles without considering social media context. However works combining the information from social media with external evidence from the wider web are lacking. To facilitate research in this direction, we release a novel dataset, PHEMEPlus, an extension of the PHEME benchmark, which contains social media conversations as well as relevant external evidence for each rumour. We demonstrate the effectiveness of incorporating such evidence in improving rumour verification models. Additionally, as part of the evidence collection, we evaluate various ways of query formulation to identify the most effective method.</abstract>
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%0 Conference Proceedings
%T PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence
%A Dougrez-Lewis, John
%A Kochkina, Elena
%A Arana-Catania, Miguel
%A Liakata, Maria
%A He, Yulan
%Y Aly, Rami
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F dougrez-lewis-etal-2022-phemeplus
%X Work on social media rumour verification utilises signals from posts, their propagation and users involved. Other lines of work target identifying and fact-checking claims based on information from Wikipedia, or trustworthy news articles without considering social media context. However works combining the information from social media with external evidence from the wider web are lacking. To facilitate research in this direction, we release a novel dataset, PHEMEPlus, an extension of the PHEME benchmark, which contains social media conversations as well as relevant external evidence for each rumour. We demonstrate the effectiveness of incorporating such evidence in improving rumour verification models. Additionally, as part of the evidence collection, we evaluate various ways of query formulation to identify the most effective method.
%R 10.18653/v1/2022.fever-1.6
%U https://aclanthology.org/2022.fever-1.6
%U https://doi.org/10.18653/v1/2022.fever-1.6
%P 49-58
Markdown (Informal)
[PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence](https://aclanthology.org/2022.fever-1.6) (Dougrez-Lewis et al., FEVER 2022)
ACL