Result Diversification for Tweet Search

Date

2014-10-14

Author

Ozsoy, Makbule Gulcin
Onal, Kezban Dilek
Altıngövde, İsmail Sengör

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Being one of the most popular microblogging platforms, Twitter handles more than two billion queries per day. Given the users' desire for fresh and novel content but their reluctance to submit long and descriptive queries, there is an inevitable need for generating diversified search results to cover different aspects of a query topic. In this paper, we address diversification of results in tweet search by adopting several methods from the text summarization and web search domains. We provide an exhaustive evaluation of all the methods using a standard dataset specifically tailored for this purpose. Our findings reveal that implicit diversification methods are more promising in the current setup, whereas explicit methods need to be augmented with a better representation of query sub-topics.

Subject Keywords

Microblogging, Tweet Search, Novelty, Diversity

URI

https://hdl.handle.net/11511/55586

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Department of Computer Engineering, Conference / Seminar