TWC: Small: User Behavior Modeling and Prediction in Anonymous Social Networks

TWC：小：匿名社交网络中的用户行为建模和预测

• 批准号: 1833642
• 负责人: Ben Zhao
• 金额: $ 30.95万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1833642&HistoricalAwards=false
• 关键词: TWC; Small; User; Behavior; Modeling

Human beings are diverse, and their online behavior is often unpredictable. In today's data-driven world, providers of online services are collecting detailed and comprehensive server-side traces of user activity. These records or logs include detailed, timestamped logs of actions taken by users, often called clickstreams. Given their scale and level of detail, clickstreams present an enormous opportunity for research into user behavioral analysis and modeling. Understanding, modeling and predicting user behavior can dramatically improve the security of today's online systems, while significantly advancing understanding of user behavior. This project develops a general platform for user behavioral modeling using clickstreams, with the goal of providing general tools for modeling user behavior in any application context. If successful, this approach will produce a generalized platform for identifying similar types of user behavior. Prior work using a similar approach already produced significant results in the context of automatically detecting fake accounts and identities in online social networks.The PIs will explore the use of clickstream similarity graphs, graphs designed to capture and model the similarity (or differences) between behavior logs of different users. By applying existing graph analysis techniques, these similarity graphs can identify general user behavioral patterns using semi-supervised learning techniques, and can be used to identify abnormal or unknown user behavior patterns. The researchers will use real detailed clickstreams from two online social networks (Renren and Whisper). The goal of the project is to make clickstream similarity graphs a general and practical user modeling tool. The project will address 3 key challenges. First, it will explore and address challenges of scale in users and trace length, so that the techniques can be applied to large user populations of hundreds of millions. Second, the project will quantify the level of dynamics in user behavior over time, developing techniques to incrementally modify or update user behavior models. Finally, the PIs will study issues in application specificity, i.e., how we can tune the tool for different dimensions of user behavior.

人类是多种多样的，他们的在线行为往往是不可预测的。在当今数据驱动的世界中，在线服务提供商正在收集详细且全面的服务器端用户活动跟踪。这些记录或日志包括用户所采取操作的详细的、带时间戳的日志，通常称为点击流。鉴于其规模和详细程度，点击流为研究用户行为分析和建模提供了巨大的机会。理解、建模和预测用户行为可以显着提高当今在线系统的安全性，同时显着增进对用户行为的理解。该项目开发了一个使用点击流进行用户行为建模的通用平台，其目标是提供在任何应用程序上下文中对用户行为进行建模的通用工具。如果成功，这种方法将产生一个通用平台，用于识别类似类型的用户行为。 之前使用类似方法的工作已经在自动检测在线社交网络中的虚假帐户和身份方面产生了显着的成果。PI 将探索点击流相似性图的使用，该图旨在捕获和建模行为之间的相似性（或差异）不同用户的日志。通过应用现有的图分析技术，这些相似图可以使用半监督学习技术来识别一般用户行为模式，并且可以用于识别异常或未知的用户行为模式。研究人员将使用来自两个在线社交网络（人人网和 Whisper）的真实详细点击流。该项目的目标是使点击流相似性图成为通用且实用的用户建模工具。该项目将解决 3 个关键挑战。 首先，它将探索并解决用户规模和轨迹长度的挑战，以便该技术可以应用于数亿的庞大用户群体。其次，该项目将量化用户行为随时间的动态水平，开发逐步修改或更新用户行为模型的技术。最后，PI 将研究应用程序特异性的问题，即我们如何针对不同维度的用户行为调整工具。