Online trendy topics detection in microblogs with selective user monitoring under cost constraints

Zhongchen Miao, Kai Chen*, Yi Zhou, Hongyuan Zha, Jianhua He, Xiaokang Yang, Wenjun Zhang

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


As microblog services such as Twitter become a fast and convenient communication approach, identification of trendy topics in microblog services has great academic and business value. However detecting trendy topics is very challenging due to huge number of users and short-text posts in microblog diffusion networks. In this paper we introduce a trendy topics detection system under computation and communication resource constraints. In stark contrast to retrieving and processing the whole microblog contents, we develop an idea of selecting a small set of microblog users and processing their posts to achieve an overall acceptable trendy topic coverage, without exceeding resource budget for detection. We formulate the selection operation of these subset users as mixed-integer optimization problems, and develop heuristic algorithms to compute their approximate solutions. The proposed system is evaluated with real-time test data retrieved from Sina Weibo, the dominant microblog service provider in China. It's shown that by monitoring 500 out of 1.6 million microblog users and tracking their microposts (about 15,000 daily) with our system, nearly 65% trendy topics can be detected, while on average 5 hours earlier before they appear in Sina Weibo official trends.

Original languageEnglish
Title of host publicationIEEE International Conference on Communications, ICC
Number of pages7
ISBN (Print)978-1-4673-6432-4
Publication statusPublished - 9 Sept 2015
EventIEEE International Conference on Communications - London, United Kingdom
Duration: 8 Jun 201512 Jun 2015


ConferenceIEEE International Conference on Communications
Abbreviated titleICC 2015
Country/TerritoryUnited Kingdom


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