识别活跃的,被动的和不活跃的目标在Twitter上的社交机器人

Mohd Fazil, M. Abulaish
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引用次数: 11

摘要

由于模仿人类行为的恶意机器人(又名社交机器人)的存在,在线社交网络正面临着严重的威胁,这些恶意机器人的成功主要是因为它们被欺骗的追随者的存在。在本文中,我们提出了一种方法,根据Twitter用户与社交机器人的互动行为,将Twitter用户分为三组——活跃的、被动的和不活跃的目标。活跃用户是那些自己关注社交机器人而不被他们关注的用户,被动用户通过关注他们来回应以下社交机器人,而不活跃用户对匿名社交机器人的以下请求没有任何兴趣。所提出的方法建模为二元和三元分类问题,其中使用表示其相同和行为方面的静态和动态组件生成用户配置文件。对现场实验收集的749个用户数据集使用了朴素贝叶斯、减少错误修剪决策树和随机森林三种不同的分类技术,并对识别出的用户类别进行了深入分析,发现活跃用户和被动用户都在频繁更新包含广告相关内容的推文。最后,利用特征排序算法对识别出的特征进行排序,分析其判别能力,发现跟随率和跟随率是最主要的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying active, reactive, and inactive targets of socialbots in Twitter
Online social networks are facing serious threats due to presence of human-behaviour imitating malicious bots (aka socialbots) that are successful mainly due to existence of their duped followers. In this paper, we propose an approach to categorize Twitter users into three groups - active, reactive, and inactive targets, based on their interaction behaviour with socialbots. Active users are those who themselves follow socialbots without being followed by them, reactive users respond to the following socialbots by following them back, whereas inactive users do not show any interest against the following requests from anonymous socialbots. The proposed approach is modelled as both binary and ternary classification problem, wherein users' profile is generated using static and dynamic components representing their identical and behavioural aspects. Three different classification techniques viz Naive Bayes, Reduced Error Pruned Decision Tree, and Random Forest are used over a dataset of 749 users collected through live experiment, and a thorough analyses of the identified users categories is presented, wherein it is found that active and reactive users keep on frequently updating their tweets containing advertising related contents. Finally, feature ranking algorithms are used to rank identified features to analyse their discriminative power, and it is found that following rate and follower rate are the most dominating features.
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