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探究微博社会机器人的构建和渗透策略。

Exploring the construction and infiltration strategies of social bots in sina microblog.

机构信息

College of Cybersecurity, Sichuan University, Chengdu, China.

Cybersecurity Research Institute, Sichuan University, Chengdu, China.

出版信息

Sci Rep. 2020 Nov 13;10(1):19821. doi: 10.1038/s41598-020-76814-8.

Abstract

Nowadays, 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.

摘要

如今,数以百万计的人使用 Twitter、Facebook 和新浪微博等在线社交网络(OSNs)来表达对时事的看法。这些 OSNs 的广泛使用也导致了社交机器人的出现。更重要的是,社交机器人的存在非常强大,以至于其中一些可以变成有影响力的用户。在本文中,我们研究了新浪微博中社交机器人的自动化构建技术和渗透策略,旨在构建友好且有影响力的社交机器人来抵御恶意解读。首先,我们研究了新浪微博数据收集的关键技术,表明其防御机制存在漏洞。然后,我们在新浪微博中构建了 96 个社交机器人,并研究了不同渗透策略的影响,如不同的属性设置和各种类型的交互。最后,我们的社交机器人在 42 天的渗透期内获得了 5546 名关注者,存活率为 100%。结果表明,我们提出的渗透策略是有效的,可以帮助社交机器人逃避新浪微博防御机制的检测。本文的研究为新浪微博防御机制敲响了警钟,并为社交机器人检测提供了有价值的参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e93/7666167/d0a650d58d6b/41598_2020_76814_Fig1_HTML.jpg

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