Cerberus: a cross-site social bot detection system based on deep learning
Online social networks attract billions of active users and deeply influence people’s lifestyles. However, as public social networks with low requirements for registration and joining, it is inevitable that social bots are able to easily register and do harmful things such as controlling public opin...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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POSTS&TELECOM PRESS Co., LTD
2024-01-01
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| Series: | 智能科学与技术学报 |
| Subjects: | |
| Online Access: | http://www.cjist.com.cn/thesisDetails?columnId=76407496&Fpath=home&index=0 |
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| _version_ | 1850194579752484864 |
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| author | TANG Jiawei LIU Yushan GAO Min GONG Qingyuan WANG Xin CHEN Yang |
| author_facet | TANG Jiawei LIU Yushan GAO Min GONG Qingyuan WANG Xin CHEN Yang |
| author_sort | TANG Jiawei |
| collection | DOAJ |
| description | Online social networks attract billions of active users and deeply influence people’s lifestyles. However, as public social networks with low requirements for registration and joining, it is inevitable that social bots are able to easily register and do harmful things such as controlling public opinion and spreading inaccurate information for profit. Nevertheless, single-site social bot detection systems often rely on historical behavioral data to identify bots, and the detection occurred after the social bots have implemented their attacks. To identify social bots as early as possible, this paper proposes Cerberus, a cross-site system for detecting social bots in social networks, which solves the cold-start problem of user identification caused by insufficient user data on a single platform at an early stage and thus identifies social bots as early as possible. In this paper, the system is designed to identify whether a user on Twitter is a bot or not by leveraging the user’s profile and text contents on his or her Medium account linked to Twitter. The results from our experiments show that the AUC score of the system can reach 0.7522, which outperforms others. |
| format | Article |
| id | doaj-art-08132af7cfc04bfb8e9f04616464d285 |
| institution | OA Journals |
| issn | 2096-6652 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 智能科学与技术学报 |
| spelling | doaj-art-08132af7cfc04bfb8e9f04616464d2852025-08-20T02:13:58ZzhoPOSTS&TELECOM PRESS Co., LTD智能科学与技术学报2096-66522024-01-0176407496Cerberus: a cross-site social bot detection system based on deep learningTANG JiaweiLIU YushanGAO MinGONG QingyuanWANG XinCHEN YangOnline social networks attract billions of active users and deeply influence people’s lifestyles. However, as public social networks with low requirements for registration and joining, it is inevitable that social bots are able to easily register and do harmful things such as controlling public opinion and spreading inaccurate information for profit. Nevertheless, single-site social bot detection systems often rely on historical behavioral data to identify bots, and the detection occurred after the social bots have implemented their attacks. To identify social bots as early as possible, this paper proposes Cerberus, a cross-site system for detecting social bots in social networks, which solves the cold-start problem of user identification caused by insufficient user data on a single platform at an early stage and thus identifies social bots as early as possible. In this paper, the system is designed to identify whether a user on Twitter is a bot or not by leveraging the user’s profile and text contents on his or her Medium account linked to Twitter. The results from our experiments show that the AUC score of the system can reach 0.7522, which outperforms others. http://www.cjist.com.cn/thesisDetails?columnId=76407496&Fpath=home&index=0Online Social Networks;Social Bot Detection;Cross-Site Linking;Deep Learning;Cold-Start Users |
| spellingShingle | TANG Jiawei LIU Yushan GAO Min GONG Qingyuan WANG Xin CHEN Yang Cerberus: a cross-site social bot detection system based on deep learning 智能科学与技术学报 Online Social Networks;Social Bot Detection;Cross-Site Linking;Deep Learning;Cold-Start Users |
| title | Cerberus: a cross-site social bot detection system
based on deep learning |
| title_full | Cerberus: a cross-site social bot detection system
based on deep learning |
| title_fullStr | Cerberus: a cross-site social bot detection system
based on deep learning |
| title_full_unstemmed | Cerberus: a cross-site social bot detection system
based on deep learning |
| title_short | Cerberus: a cross-site social bot detection system
based on deep learning |
| title_sort | cerberus a cross site social bot detection system based on deep learning |
| topic | Online Social Networks;Social Bot Detection;Cross-Site Linking;Deep Learning;Cold-Start Users |
| url | http://www.cjist.com.cn/thesisDetails?columnId=76407496&Fpath=home&index=0 |
| work_keys_str_mv | AT tangjiawei cerberusacrosssitesocialbotdetectionsystembasedondeeplearning AT liuyushan cerberusacrosssitesocialbotdetectionsystembasedondeeplearning AT gaomin cerberusacrosssitesocialbotdetectionsystembasedondeeplearning AT gongqingyuan cerberusacrosssitesocialbotdetectionsystembasedondeeplearning AT wangxin cerberusacrosssitesocialbotdetectionsystembasedondeeplearning AT chenyang cerberusacrosssitesocialbotdetectionsystembasedondeeplearning |