Author:
Wang Wenxian,Chen Xingshu,Jiang Shuyu,Wang Haizhou,Yin Mingyong,Wang Peiming
Abstract
AbstractNowadays, millions of people use Online Social Networks (OSNs) like Twitter, Facebook and Sina Microblog, to express opinions on current events. The widespread use of these OSNs has also led to the emergence of social bots. What is more, the existence of social bots is so powerful that some of them can turn into influential users. In this paper, we studied the automated construction technology and infiltration strategies of social bots in Sina Microblog, aiming at building friendly and influential social bots to resist malicious interpretations. Firstly, we studied the critical technology of Sina Microblog data collection, which indicates that the defense mechanism of that is vulnerable. Then, we constructed 96 social bots in Sina Microblog and researched the influence of different infiltration strategies, like different attribute settings and various types of interactions. Finally, our social bots gained 5546 followers in the 42-day infiltration period with a 100% survival rate. The results show that the infiltration strategies we proposed are effective and can help social bots escape detection of Sina Microblog defense mechanism as well. The study in this paper sounds an alarm for Sina Microblog defense mechanism and provides a valuable reference for social bots detection.
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Springer Science and Business Media LLC
Reference66 articles.
1. The state of digital in april 2019: All the numbers you need to know. https://wearesocial.com/blog/2019/04/the-state-of-digital-in-april-2019-all-the-numbers-you-need-to-know. Accessd May 1, 2020.
2. Hui, L. Weibo reports robust q2 user growth. http://www.xinhuanet.com/english/2019-08/20/c_138323288.htm. Accessd May 1, 2020.
3. 2019 sina microblog rumor refutation data report. https://m.weibo.cn/detail/4462758332079552. Accessd August 24, 2020.
4. Li, Q., Zhang, Q. & Si, L. Rumor detection by exploiting user credibility information, attention and multi-task learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 1173–1179 (2019).
5. Schmidt, A. & Wiegand, M. A survey on hate speech detection using natural language processing. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media 1–10 (2017).
Cited by
9 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献