@inproceedings{zeng-gao-2022-early,
title = "{E}arly Rumor Detection Using Neural {H}awkes Process with a New Benchmark Dataset",
author = "Zeng, Fengzhu and
Gao, Wei",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.302/",
doi = "10.18653/v1/2022.naacl-main.302",
pages = "4105--4117",
abstract = "Little attention has been paid on EArly Rumor Detection (EARD), and EARD performance was evaluated inappropriately on a few datasets where the actual early-stage information is largely missing. To reverse such situation, we construct BEARD, a new Benchmark dataset for EARD, based on claims from fact-checking websites by trying to gather as many early relevant posts as possible. We also propose HEARD, a novel model based on neural Hawkes process for EARD, which can guide a generic rumor detection model to make timely, accurate and stable predictions. Experiments show that HEARD achieves effective EARD performance on two commonly used general rumor detection datasets and our BEARD dataset."
}
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%0 Conference Proceedings
%T Early Rumor Detection Using Neural Hawkes Process with a New Benchmark Dataset
%A Zeng, Fengzhu
%A Gao, Wei
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F zeng-gao-2022-early
%X Little attention has been paid on EArly Rumor Detection (EARD), and EARD performance was evaluated inappropriately on a few datasets where the actual early-stage information is largely missing. To reverse such situation, we construct BEARD, a new Benchmark dataset for EARD, based on claims from fact-checking websites by trying to gather as many early relevant posts as possible. We also propose HEARD, a novel model based on neural Hawkes process for EARD, which can guide a generic rumor detection model to make timely, accurate and stable predictions. Experiments show that HEARD achieves effective EARD performance on two commonly used general rumor detection datasets and our BEARD dataset.
%R 10.18653/v1/2022.naacl-main.302
%U https://aclanthology.org/2022.naacl-main.302/
%U https://doi.org/10.18653/v1/2022.naacl-main.302
%P 4105-4117
Markdown (Informal)
[Early Rumor Detection Using Neural Hawkes Process with a New Benchmark Dataset](https://aclanthology.org/2022.naacl-main.302/) (Zeng & Gao, NAACL 2022)
ACL