Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2015

Abstract

Micro-reviews is a new type of user-generated content arising from the prevalence of mobile devices and social media in the past few years. Micro-reviews are bite-size reviews (usually under 200 characters), commonly posted on social media or check-in services, using a mobile device. They capture the immediate reaction of users, and they are rich in information, concise, and to the point. However, the abundance of micro-reviews, and their telegraphic nature make it increasingly difficult to go through them and extract the useful information, especially on a mobile device. In this paper, we address the problem of summarizing the micro-reviews of an entity, such that the summary is representative, compact, and readable. We formulate the summarization problem as that of synthesizing a new "review" using snippets of full-text reviews. To produce a summary that naturally balances compactness and representativeness, we work within the Minimum Description Length framework. We show that finding the optimal summary is NP-hard, and we consider approximation and heuristic algorithms. We perform a thorough evaluation of our methodology on real-life data collected from Foursquare and Yelp. We demonstrate that our summaries outperform individual reviews, as well as existing summarization approaches.

Keywords

social context, social media, social search

Discipline

Computer Sciences | Databases and Information Systems | Social Media

Publication

WSDM '15: Proceedings of the 8th ACM International Conference on Web Search and Data Mining: 31 January-6 February 2015, Shanghai

First Page

169

Last Page

178

ISBN

9781450333177

Identifier

10.1145/2684822.2685321

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/2684822.2685321

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