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首页> 外文会议>2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics >Identifying user behavior on Twitter based on multi-scale entropy
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Identifying user behavior on Twitter based on multi-scale entropy

机译：基于多尺度熵识别Twitter上的用户行为

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摘要

Twitter as an online social network is used for many reasons, including information dissemination, marketing, political organizing, spamming, promotion, conversations and so on. Characterizing these activities and categorizing users is a challenging task. Traditional user classification models are based on individual user's profile information such as age, location, register time, interests and tweets, which have not considered the whole complexity of posting behavior. In this paper we introduce Multi-scale Entropy for analyzing and identifying user behavior on Twitter, and separate users to different categories. We have identified five distinct categories of tweeting activity on Twitter: individual activity, newsworthy information dissemination activity, advertising and promotion activity, automatic/robotic activity and other activities. Through the experiment we achieved good separation of different activities of these five categories based on Multi-scale Entropy of users' posting time series. The method based on Multi-scale Entropy is computationally efficient; it has many applications, including automatic spam-detection, trend identification, trust management, user-modeling in online social media.
机译：使用Twitter作为在线社交网络的原因很多，包括信息传播，市场营销，政治组织，垃圾邮件，促销，对话等。对这些活动进行表征并对用户进行分类是一项艰巨的任务。传统的用户分类模型基于个人用户的个人资料信息，例如年龄，位置，注册时间，兴趣和推文，这些信息并未考虑过发布行为的整体复杂性。在本文中，我们介绍了用于在Twitter上分析和识别用户行为的多尺度熵，并将用户分为不同的类别。我们已经在Twitter上确定了五个不同的推文活动类别：个人活动，具有新闻价值的信息传播活动，广告和促销活动，自动/机器人活动以及其他活动。通过实验，我们基于用户发布时间序列的多尺度熵，很好地分离了这五个类别的不同活动。基于多尺度熵的方法计算效率高。它具有许多应用程序，包括自动垃圾邮件检测，趋势识别，信任管理，在线社交媒体中的用户建模。

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