Just in Time: A Social Computing Approach for Finding Reliable Answers in Large Public Spaces
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.
KeywordsSocial computing Mobile social network Context–aware Q&A
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