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- W2169114488 abstract "The prevalence of social media applications is generating potentially large personal archives of posts, tweets, and other communications. The existence of these archives creates a need for search tools, which can be seen as an extension of current desktop search services. Little is currently known about the best search techniques for personal archives of social data, because of the difficulty of creating test collections. In this paper, we describe how test collections for personal social data can be created by using games to collect queries. We then compare a range of retrieval models that exploit the semi-structured nature of social data. Our results show that a mixture of language models with field distribution estimation can be effective for this type of data, with certain fields, such as the name of the poster, being particularly important. We also analyze the properties of the queries that were generated by users with two versions of the games." @default.
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- W2169114488 date "2012-02-08" @default.
- W2169114488 modified "2023-10-16" @default.
- W2169114488 title "Evaluating search in personal social media collections" @default.
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- W2169114488 doi "https://doi.org/10.1145/2124295.2124375" @default.
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