Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2022
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.
Discipline
Databases and Information Systems | Programming Languages and Compilers
Research Areas
Data Science and Engineering
Publication
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, Seattle, United States, July 10-15
First Page
4105
Last Page
4117
Identifier
10.18653/v1/2022.naacl-main.302
Publisher
Association for Computational Linguistics
City or Country
Seattle, United States
Citation
ZENG, Fengzhu and GAO, Wei.
Early rumor detection using neural Hawkes process with a new benchmark dataset. (2022). Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, Seattle, United States, July 10-15. 4105-4117.
Available at: https://ink.library.smu.edu.sg/sis_research/7604
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.