Cerberus: cross-site social bot detection system based on deep learning
Social networking sites have attracted billions of users and influence people's lifestyles. However, as open platform 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 opinions and s...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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POSTS&TELECOM PRESS Co., LTD
2024-12-01
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| Series: | 智能科学与技术学报 |
| Subjects: | |
| Online Access: | http://www.cjist.com.cn/zh/article/doi/10.11959/j.issn.2096-6652.202436/ |
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| _version_ | 1832586369392705536 |
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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 | Social networking sites have attracted billions of users and influence people's lifestyles. However, as open platform 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 opinions 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 proposed Cerberus, a cross-site social bot detection system. Cerberus can solve the cold-start problem of user identification caused by insufficient user data on a single platform at an early stage. Cerberus used personal information and historical activity on the Medium website of users to make prediction about whether a user's account on Twitter was a social bot. The results from our experiments show that the AUC score of Cerberus can reach 0.7522, which has good recognition accuracy. |
| format | Article |
| id | doaj-art-c8f71d6772df4e2993f94e8f99dc2661 |
| institution | Kabale University |
| issn | 2096-6652 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 智能科学与技术学报 |
| spelling | doaj-art-c8f71d6772df4e2993f94e8f99dc26612025-01-25T19:00:17ZzhoPOSTS&TELECOM PRESS Co., LTD智能科学与技术学报2096-66522024-12-01648249476440802Cerberus: cross-site social bot detection system based on deep learningTANG JiaweiLIU YushanGAO MinGONG QingyuanWANG XinCHEN YangSocial networking sites have attracted billions of users and influence people's lifestyles. However, as open platform 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 opinions 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 proposed Cerberus, a cross-site social bot detection system. Cerberus can solve the cold-start problem of user identification caused by insufficient user data on a single platform at an early stage. Cerberus used personal information and historical activity on the Medium website of users to make prediction about whether a user's account on Twitter was a social bot. The results from our experiments show that the AUC score of Cerberus can reach 0.7522, which has good recognition accuracy.http://www.cjist.com.cn/zh/article/doi/10.11959/j.issn.2096-6652.202436/online social networkSocial Bot Detectioncross-site linkingdeep learningcold-start user |
| spellingShingle | TANG Jiawei LIU Yushan GAO Min GONG Qingyuan WANG Xin CHEN Yang Cerberus: cross-site social bot detection system based on deep learning 智能科学与技术学报 online social network Social Bot Detection cross-site linking deep learning cold-start user |
| title | Cerberus: cross-site social bot detection system based on deep learning |
| title_full | Cerberus: cross-site social bot detection system based on deep learning |
| title_fullStr | Cerberus: cross-site social bot detection system based on deep learning |
| title_full_unstemmed | Cerberus: cross-site social bot detection system based on deep learning |
| title_short | Cerberus: cross-site social bot detection system based on deep learning |
| title_sort | cerberus cross site social bot detection system based on deep learning |
| topic | online social network Social Bot Detection cross-site linking deep learning cold-start user |
| url | http://www.cjist.com.cn/zh/article/doi/10.11959/j.issn.2096-6652.202436/ |
| work_keys_str_mv | AT tangjiawei cerberuscrosssitesocialbotdetectionsystembasedondeeplearning AT liuyushan cerberuscrosssitesocialbotdetectionsystembasedondeeplearning AT gaomin cerberuscrosssitesocialbotdetectionsystembasedondeeplearning AT gongqingyuan cerberuscrosssitesocialbotdetectionsystembasedondeeplearning AT wangxin cerberuscrosssitesocialbotdetectionsystembasedondeeplearning AT chenyang cerberuscrosssitesocialbotdetectionsystembasedondeeplearning |