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Profile characteristics of fake Twitter accounts

机译:假Twitter帐户的个人资料特征

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In online social networks, the audience size commanded by an organization or an individual is a critical measure of that entity?¢????s popularity and this measure has important economic and/or political implications. Such efforts to measure popularity of users or exploit knowledge about their audience are complicated by the presence of fake profiles on these networks. In this study, analysis of 62 million publicly available Twitter user profiles was conducted and a strategy to identify automatically generated fake profiles was established. Using a combination of a pattern-matching algorithm on screen-names and an analysis of update times, a reasonable number (?¢????0.1% of total users) of highly reliable fake user accounts were identified. Analysis of profile creation times and URLs of these fake accounts revealed their distinct behavior relative to a ground truth data set. The characteristics of friends and followers of users in the two data sets further revealed the very different nature of the two groups. The ratio of number of followers-to-friends for ground truth users was ?¢????1, consistent with past observations, while the fake profiles had a median ratio ?¢????30, indicating that the fake users we identified were primarily focused on gathering friends. An analysis of the temporal evolution of accounts over 2 years showed that the friends-to-followers ratio increased over time for fake profiles while they decreased for ground truth users. Our results, thus, suggest that a profile-based approach can be used for identifying a core set of fake online social network users in a time-efficient manner.
机译:在在线社交网络中,由组织或个人命令的受众人数是该实体受欢迎程度的关键指标,并且该指标具有重要的经济和/或政治意义。这些网络上存在伪造的配置文件,使得衡量用户受欢迎程度或利用有关他们的受众的知识的努力变得复杂。在这项研究中,对6,200万个公开可用的Twitter用户个人资料进行了分析,并建立了一种识别自动生成的伪造个人资料的策略。通过对屏幕名称使用模式匹配算法并分析更新时间,可以确定合理数量(占总用户的0.1%)高度可靠的假用户帐户。对这些伪造帐户的配置文件创建时间和URL进行的分析显示,它们相对于基本事实数据集的独特行为。在两个数据集中,用户的朋友和追随者的特征进一步揭示了两组的性质完全不同。地面真实用户的追随者与朋友的比率为1,与过去的观察结果一致,而虚假资料的中位数比率为30,表明我们的虚假用户确定主要集中在收集朋友上。对过去2年的帐户时间演变的分析表明,对于假的个人资料,朋友与从人的比率随着时间的推移而增加,而对真实情况使用者的朋友与跟随者的比率则随着时间而减少。因此,我们的结果表明,基于配置文件的方法可用于以省时的方式识别一组伪造的在线社交网络用户。

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