Cerberus: cross-site social bot detection system based on deep learning

TANG Jiawei; LIU Yushan; GAO Min; GONG Qingyuan; WANG Xin; CHEN Yang

doi:10.11959/j.issn.2096-6652.202436

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Cerberus: cross-site social bot detection system based on deep learning

Social Computing and Social Intelligence | 更新时间：2025-01-24

- Cerberus: cross-site social bot detection system based on deep learning
- Chinese Journal of Intelligent Science and Technology Vol. 6, Issue 4, Pages: 482-494(2024)
- 作者机构：
  
  1.复旦大学计算机科学技术学院，上海 200438
  2.复旦大学智能复杂体系基础理论与关键技术实验室，上海 200438
- 作者简介：
- 基金信息：
  
  National Key R&D Program of China(2022YFB3102901);National Natural Science Foundation of China(62072115;62102094);Shanghai Science and Technology Innovation Action Plan(22510713600)
- DOI：10.11959/j.issn.2096-6652.202436
  CLC： TP39
- Received：07 August 2024，
  
  Revised：2024-09-29，
  
  Published：15 December 2024
- 稿件说明：
移动端阅览
汤家伟,刘育杉,高敏等.Cerberus：基于深度学习的跨网站社交机器人检测系统[J].智能科学与技术学报,2024,06(04):482-494.

TANG Jiawei,LIU Yushan,GAO Min,et al.Cerberus: cross-site social bot detection system based on deep learning[J].Chinese Journal of Intelligent Science and Technology,2024,06(04):482-494.
汤家伟,刘育杉,高敏等.Cerberus：基于深度学习的跨网站社交机器人检测系统[J].智能科学与技术学报,2024,06(04):482-494. DOI： 10.11959/j.issn.2096-6652.202436.

TANG Jiawei,LIU Yushan,GAO Min,et al.Cerberus: cross-site social bot detection system based on deep learning[J].Chinese Journal of Intelligent Science and Technology,2024,06(04):482-494. DOI： 10.11959/j.issn.2096-6652.202436.

摘要

社交网站吸引了数十亿用户，影响着人们的生活方式。社交网站作为开放平台，注册加入的门槛较低，社交机器人能够轻易地注册，并进行舆论导向控制、不实信息传播等有害活动，以谋取利益。单一社交网站的机器人检测系统往往需要依赖用户的历史行为数据进行分析。因此，社交机器人在被识别出之前往往已经成功实施了恶意攻击。为尽早地识别出社交机器人，提出了跨网站社交机器人检测系统Cerberus。Cerberus可以解决用户早期在单个社交网站上数据不充足导致的用户识别“冷启动”的问题。Cerberus使用用户在Medium网站上的个人信息和历史活动信息，对用户链接在Twitter上的账号是否为社交机器人账号进行预测。结果表明，该系统的AUC值可达0.7552，具有良好的识别准确性。

Abstract

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.

关键词

Keywords

references

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