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Analyzing the Influence of Instant Messaging on User Relationship Estimation

机译:分析即时消息对用户关系估算的影响

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Recent years have seen a plethora of new mobile social networking services, given the widespread and ubiquitous availability of smartphones. However, from a user's perspective, two fundamental problems underlie these services: Undesired interruptions and privacy violations. Understanding user relationships can help smartphones to provide appropriate decision support for improved notification management and content sharing, thus mitigating these negative effects. In this work, we investigate the influence of mobile instant messaging (IM) services in estimating the type and strength of user relationships. To this end, we implemented an Android-based application to gather users' historical communication data and ran a study to collect manual assessments for each smartphone contact. Our user study shows that friends and hobby-related contacts tend to communicate more using IM services, whereas family and work-related contacts tend to use calls. Furthermore, our machine learning models estimate the social circles with an average accuracy of 77%, and distinguish between strong and weak relationships with an average accuracy of 76%, therein.
机译:鉴于智能手机的广泛和无处不在的可用性,近年来已经看到了一流的新移动社交网络服务。但是,从用户的角度来看,两个基本问题借给了这些服务:不希望的中断和隐私违规行为。了解用户关系可以帮助智能手机为改进的通知管理和内容共享提供适当的决策支持,从而减轻这些负效应。在这项工作中,我们调查了移动即时消息(IM)服务在估计用户关系的类型和强度方面的影响。为此,我们实施了基于Android的应用程序来收集用户的历史通信数据,并运行研究以收集每个智能手机联系人的手动评估。我们的用户学习表明,朋友和业余爱好相关联系人往往会使用IM服务进行更多信息,而家庭和与工作相关联系人往往会使用呼叫。此外,我们的机器学习模型估计了平均精度为77%的社交界,并区分了在其中的平均准确性为76%的强大和弱关系。

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