Social Bot-Aware Graph Neural Network for Early Rumor Detection
Zhen Huang, Zhilong Lv, Xiaoyun Han, Binyang Li, Menglong Lu, Dongsheng Li
Abstract
Early rumor detection is a key challenging task to prevent rumors from spreading widely. Sociological research shows that social bots’ behavior in the early stage has become the main reason for rumors’ wide spread. However, current models do not explicitly distinguish genuine users from social bots, and their failure in identifying rumors timely. Therefore, this paper aims at early rumor detection by accounting for social bots’ behavior, and presents a Social Bot-Aware Graph Neural Network, named SBAG. SBAG firstly pre-trains a multi-layer perception network to capture social bot features, and then constructs multiple graph neural networks by embedding the features to model the early propagation of posts, which is further used to detect rumors. Extensive experiments on three benchmark datasets show that SBAG achieves significant improvements against the baselines and also identifies rumors within 3 hours while maintaining more than 90% accuracy.- Anthology ID:
- 2022.coling-1.580
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6680–6690
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.580
- DOI:
- Bibkey:
- Cite (ACL):
- Zhen Huang, Zhilong Lv, Xiaoyun Han, Binyang Li, Menglong Lu, and Dongsheng Li. 2022. Social Bot-Aware Graph Neural Network for Early Rumor Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6680–6690, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Social Bot-Aware Graph Neural Network for Early Rumor Detection (Huang et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.580.pdf
- Code
- sky-star-moon/sbag
Export citation
@inproceedings{huang-etal-2022-social,
title = "Social Bot-Aware Graph Neural Network for Early Rumor Detection",
author = "Huang, Zhen and
Lv, Zhilong and
Han, Xiaoyun and
Li, Binyang and
Lu, Menglong and
Li, Dongsheng",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.580",
pages = "6680--6690",
abstract = "Early rumor detection is a key challenging task to prevent rumors from spreading widely. Sociological research shows that social bots{'} behavior in the early stage has become the main reason for rumors{'} wide spread. However, current models do not explicitly distinguish genuine users from social bots, and their failure in identifying rumors timely. Therefore, this paper aims at early rumor detection by accounting for social bots{'} behavior, and presents a Social Bot-Aware Graph Neural Network, named SBAG. SBAG firstly pre-trains a multi-layer perception network to capture social bot features, and then constructs multiple graph neural networks by embedding the features to model the early propagation of posts, which is further used to detect rumors. Extensive experiments on three benchmark datasets show that SBAG achieves significant improvements against the baselines and also identifies rumors within 3 hours while maintaining more than 90{\%} accuracy.",
}
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<abstract>Early rumor detection is a key challenging task to prevent rumors from spreading widely. Sociological research shows that social bots’ behavior in the early stage has become the main reason for rumors’ wide spread. However, current models do not explicitly distinguish genuine users from social bots, and their failure in identifying rumors timely. Therefore, this paper aims at early rumor detection by accounting for social bots’ behavior, and presents a Social Bot-Aware Graph Neural Network, named SBAG. SBAG firstly pre-trains a multi-layer perception network to capture social bot features, and then constructs multiple graph neural networks by embedding the features to model the early propagation of posts, which is further used to detect rumors. Extensive experiments on three benchmark datasets show that SBAG achieves significant improvements against the baselines and also identifies rumors within 3 hours while maintaining more than 90% accuracy.</abstract>
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%0 Conference Proceedings %T Social Bot-Aware Graph Neural Network for Early Rumor Detection %A Huang, Zhen %A Lv, Zhilong %A Han, Xiaoyun %A Li, Binyang %A Lu, Menglong %A Li, Dongsheng %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F huang-etal-2022-social %X Early rumor detection is a key challenging task to prevent rumors from spreading widely. Sociological research shows that social bots’ behavior in the early stage has become the main reason for rumors’ wide spread. However, current models do not explicitly distinguish genuine users from social bots, and their failure in identifying rumors timely. Therefore, this paper aims at early rumor detection by accounting for social bots’ behavior, and presents a Social Bot-Aware Graph Neural Network, named SBAG. SBAG firstly pre-trains a multi-layer perception network to capture social bot features, and then constructs multiple graph neural networks by embedding the features to model the early propagation of posts, which is further used to detect rumors. Extensive experiments on three benchmark datasets show that SBAG achieves significant improvements against the baselines and also identifies rumors within 3 hours while maintaining more than 90% accuracy. %U https://aclanthology.org/2022.coling-1.580 %P 6680-6690
Markdown (Informal)
[Social Bot-Aware Graph Neural Network for Early Rumor Detection](https://aclanthology.org/2022.coling-1.580) (Huang et al., COLING 2022)
- Social Bot-Aware Graph Neural Network for Early Rumor Detection (Huang et al., COLING 2022)
ACL
- Zhen Huang, Zhilong Lv, Xiaoyun Han, Binyang Li, Menglong Lu, and Dongsheng Li. 2022. Social Bot-Aware Graph Neural Network for Early Rumor Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6680–6690, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.