Early rumor detection using neural Hawkes process with a new benchmark dataset

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...

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Main Authors: ZENG, Fengzhu, GAO, Wei
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/sis_research/7604
https://ink.library.smu.edu.sg/context/sis_research/article/8607/viewcontent/2022.naacl_main.302.pdf
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spelling sg-smu-ink.sis_research-86072022-12-22T03:32:03Z Early rumor detection using neural Hawkes process with a new benchmark dataset ZENG, Fengzhu GAO, Wei 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. 2022-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7604 info:doi/10.18653/v1/2022.naacl-main.302 https://ink.library.smu.edu.sg/context/sis_research/article/8607/viewcontent/2022.naacl_main.302.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Programming Languages and Compilers
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Programming Languages and Compilers
spellingShingle Databases and Information Systems
Programming Languages and Compilers
ZENG, Fengzhu
GAO, Wei
Early rumor detection using neural Hawkes process with a new benchmark dataset
description 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.
format text
author ZENG, Fengzhu
GAO, Wei
author_facet ZENG, Fengzhu
GAO, Wei
author_sort ZENG, Fengzhu
title Early rumor detection using neural Hawkes process with a new benchmark dataset
title_short Early rumor detection using neural Hawkes process with a new benchmark dataset
title_full Early rumor detection using neural Hawkes process with a new benchmark dataset
title_fullStr Early rumor detection using neural Hawkes process with a new benchmark dataset
title_full_unstemmed Early rumor detection using neural Hawkes process with a new benchmark dataset
title_sort early rumor detection using neural hawkes process with a new benchmark dataset
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/sis_research/7604
https://ink.library.smu.edu.sg/context/sis_research/article/8607/viewcontent/2022.naacl_main.302.pdf
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