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Just in Time: A Social Computing Approach for Finding Reliable Answers in Large Public Spaces

机译:正好及时:在大型公共空间中找到可靠答案的社交计算方法

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While people are away from their regular social terrain, they are usually exposed to newer situations, where they often need to seek for information or help. Nowadays people can find information from the Internet by using smartphones even when they are traveling. However, for many real-life questions, the Internet is not a suitable source of a 'reliable answer,' especially when the information-need or the question is too context-sensitive. Furthermore, it is also difficult to compose a context-sensitive real-life question effectively to find suitable answers. Therefore, along with other reasons, such as, individual's ability or experience, people seek for help or assistance from other people. And most of the cases, they need personalized support which is tailored for a particular context. With the recent growth of computer mediated online social networks, people can relatively easily ask their social peers for help. However, these networks are not yet suitable for composing questions with rich-media (e.g., with audio) and with contextual information. Aspects of a question (e.g., timeliness, demand for details) become much clearer when the context in which a question being asked is exchanged. In order to address this, a Social Computing system called Just in Time has been developed which is a context and social aware 'question-and-answer' system. It utilizes the users' context and social network to formulate a question. It helps the users to get answers from trustable social peers. A formative evaluation was conducted with a small number of users that used the system for two days. Some interesting side effects were observed, such as, users started using the system as a context-aware 'instant messaging' system. It showed that there is a clear benefit in sharing context in order to get relevant answers. And the result was inspiring for further development of social computing research works.
机译:虽然人们远离他们的常规社交地形,但它们通常会暴露在更新的情况下,他们经常需要寻求信息或帮助。如今,人们即使在旅行时也可以使用智能手机从互联网上找到信息。然而,对于许多现实生活问题,互联网不是“可靠答案”的合适来源,尤其是当信息需要或问题过于上下文敏感时。此外,还难以有效地构成上下文敏感的现实生活,以找到合适的答案。因此,以及其他原因,例如个人的能力或经历,人们寻求其他人的帮助或帮助。而且大多数情况下,他们需要针对特定​​上下文量身定制的个性化支持。随着最近计算机调解的在线社交网络的增长,人们可以相对容易地向其社会同行寻求帮助。但是,这些网络尚不适用于与富裕媒体(例如,带有音频)和上下文信息构成问题。问题的各个方面(例如,及时性,细节需求)在被换取问题的上下文时变得更加清晰。为了解决这一点,已经开发了一个刚刚调用的社交计算系统,这是一个上下文和社会知识的“问题和答案”系统。它利用用户的上下文和社交网络来制定一个问题。它帮助用户从可信的社交同行获得答案。使用少量使用该系统进行两天的用户进行形成性评估。观察到一些有趣的副作用,例如,用户开始使用该系统作为上下文感知的“即时消息”系统。它表明,共享背景下存在明确的益处,以获得相关答案。结果是鼓励社会计算研究工作的进一步发展。

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