通过对社交网络痕迹的差异分析,发现和预测用户的日常行为

Fabio Pianese, Xueli An, F. Kawsar, H. Ishizuka
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引用次数: 44

摘要

人类活动模式的研究传统上依赖于对用户位置的持续跟踪。我们从一个新的角度来探讨活动模式发现问题,这一问题正迅速引起人们的关注。我们不是主动地对不断增加的传感器数据进行采样,而是探索多个移动社交网络的参与式传感潜力,在这些网络上,用户经常披露他们的位置和他们访问的场所的信息。在本文中,我们提出了过滤、聚合和处理社交网络痕迹的自动化技术,目的是提取有规律发生的用户活动的描述,我们将其称为“用户例程”。我们报告的发现是基于两个关于单个用户池的本地化数据集:前者包含公共地理标记的Twitter消息,后者包含Foursquare签到信息,为我们提供有关我们观察到的位置的有意义的地点信息。我们分析并结合两个数据集来突出它们的属性,并展示紧急特征如何增强我们对用户日常安排的理解。最后,我们评估和讨论了常规描述在预测未来用户活动和位置方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discovering and predicting user routines by differential analysis of social network traces
The study of human activity patterns traditionally relies on the continuous tracking of user location. We approach the problem of activity pattern discovery from a new perspective which is rapidly gaining attention. Instead of actively sampling increasing volumes of sensor data, we explore the participatory sensing potential of multiple mobile social networks, on which users often disclose information about their location and the venues they visit. In this paper, we present automated techniques for filtering, aggregating, and processing combined social networking traces with the goal of extracting descriptions of regularly-occurring user activities, which we refer to as “user routines”. We report our findings based on two localized data sets about a single pool of users: the former contains public geotagged Twitter messages, the latter Foursquare check-ins that provide us with meaningful venue information about the locations we observe. We analyze and combine the two datasets to highlight their properties and show how the emergent features can enhance our understanding of users' daily schedule. Finally, we evaluate and discuss the potential of routine descriptions for predicting future user activity and location.
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