基于意图嵌入的社交机器人检测方法

牛红峰; 李嘉伟; 宋云鹏; 蔡忠闽

doi:10.11959/j.issn.1000-436x.2024205

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基于意图嵌入的社交机器人检测方法

学术论文 | 更新时间：2024-12-24

- 基于意图嵌入的社交机器人检测方法
- Intention embedding method based social bot detection
- 通信学报 2024年45卷第11期页码：194-205
- 作者机构：
  
  1.西安交通大学智能网络与网络安全教育部重点实验室，陕西西安 710049
  2.西安交通大学自动化科学与工程学院，陕西西安 710049
- 作者简介：
  
  [ "牛红峰（1989- ），男，河南新乡人，西安交通大学博士生，主要研究方向为网络安全与人机交互。" ]
  [ "李嘉伟（2000- ），男，河北沧州人，西安交通大学硕士生，主要研究方向为网络安全与人机交互。" ]
  [ "宋云鹏（1990- ），男，陕西宝鸡人，博士，西安交通大学副教授、硕士生导师，主要研究方向为人机交互、安全与隐私、混合增强智能。" ]
  [ "蔡忠闽（1975- ），男，福建晋江人，博士，西安交通大学教授、博士生导师，主要研究方向为智能人机交互、混合增强智能、电力系统智能化、虚拟现实和增强现实。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62102308)
- DOI：10.11959/j.issn.1000-436x.2024205
  中图分类号： TP301.6
- 收稿日期：2024-07-10，
  
  修回日期：2024-11-11，
  
  纸质出版日期：2024-11-25
- 稿件说明：
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牛红峰,李嘉伟,宋云鹏等.基于意图嵌入的社交机器人检测方法[J].通信学报,2024,45(11):194-205.

NIU Hongfeng,LI Jiawei,SONG Yunpeng,et al.Intention embedding method based social bot detection[J].Journal on Communications,2024,45(11):194-205.
牛红峰,李嘉伟,宋云鹏等.基于意图嵌入的社交机器人检测方法[J].通信学报,2024,45(11):194-205. DOI： 10.11959/j.issn.1000-436x.2024205.

NIU Hongfeng,LI Jiawei,SONG Yunpeng,et al.Intention embedding method based social bot detection[J].Journal on Communications,2024,45(11):194-205. DOI： 10.11959/j.issn.1000-436x.2024205.

摘要

人工智能生成内容技术显著提升了社交机器人的伪装能力，给现有的机器人检测方法带来新的挑战。通过对社交平台用户意图进行建模，提出一种基于意图嵌入的社交机器人检测方法，从而避免直接在行为层面检测伪装能力大幅提升的机器人这一难题。实验结果表明，采用意图嵌入的检测模型相比未使用意图嵌入的模型，社交机器人检测准确率提高了5.58个百分点，并增强了对不同类型社交机器人的识别能力，验证了意图嵌入在提升人机检测任务性能中的有效性。

Abstract

Artificial intelligence generated content technology has significantly enhanced the disguise capabilities of social bots

presenting new challenges to existing bot detection methods. By modeling the intentions of social media users through intention representation

a intention embedding method based social bot detection was proposed

thereby avoiding the difficulty of directly detecting bots with enhanced behavioral camouflage at the action level on social platforms. Experimental results show that the detection model using intention embedding improves the accuracy of social bot detection by 5.58 percentage points compared to models not utilizing intention embedding

and it enhances the recognition capability of specific types of social bots

verifying the effectiveness of intention embedding in improving the performance of human-bot detection tasks.

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Keywords

references

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