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.", }
<?xml version="1.0" encoding="UTF-8"?> <modsCollection xmlns="http://www.loc.gov/mods/v3"> <mods ID="huang-etal-2022-social"> <titleInfo> <title>Social Bot-Aware Graph Neural Network for Early Rumor Detection</title> </titleInfo> <name type="personal"> <namePart type="given">Zhen</namePart> <namePart type="family">Huang</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Zhilong</namePart> <namePart type="family">Lv</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Xiaoyun</namePart> <namePart type="family">Han</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Binyang</namePart> <namePart type="family">Li</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Menglong</namePart> <namePart type="family">Lu</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Dongsheng</namePart> <namePart type="family">Li</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <originInfo> <dateIssued>2022-10</dateIssued> </originInfo> <typeOfResource>text</typeOfResource> <relatedItem type="host"> <titleInfo> <title>Proceedings of the 29th International Conference on Computational Linguistics</title> </titleInfo> <name type="personal"> <namePart type="given">Nicoletta</namePart> <namePart type="family">Calzolari</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Chu-Ren</namePart> <namePart type="family">Huang</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Hansaem</namePart> <namePart type="family">Kim</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">James</namePart> <namePart type="family">Pustejovsky</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Leo</namePart> <namePart type="family">Wanner</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Key-Sun</namePart> <namePart type="family">Choi</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Pum-Mo</namePart> <namePart type="family">Ryu</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Hsin-Hsi</namePart> <namePart type="family">Chen</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Lucia</namePart> <namePart type="family">Donatelli</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Heng</namePart> <namePart type="family">Ji</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Sadao</namePart> <namePart type="family">Kurohashi</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Patrizia</namePart> <namePart type="family">Paggio</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Nianwen</namePart> <namePart type="family">Xue</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Seokhwan</namePart> <namePart type="family">Kim</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Younggyun</namePart> <namePart type="family">Hahm</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Zhong</namePart> <namePart type="family">He</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Tony</namePart> <namePart type="given">Kyungil</namePart> <namePart type="family">Lee</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Enrico</namePart> <namePart type="family">Santus</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Francis</namePart> <namePart type="family">Bond</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Seung-Hoon</namePart> <namePart type="family">Na</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <originInfo> <publisher>International Committee on Computational Linguistics</publisher> <place> <placeTerm type="text">Gyeongju, Republic of Korea</placeTerm> </place> </originInfo> <genre authority="marcgt">conference publication</genre> </relatedItem> <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> <identifier type="citekey">huang-etal-2022-social</identifier> <location> <url>https://aclanthology.org/2022.coling-1.580</url> </location> <part> <date>2022-10</date> <extent unit="page"> <start>6680</start> <end>6690</end> </extent> </part> </mods> </modsCollection>
%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.