Aggregate estimation over a microblog platform

Saravanan Thirumuruganathan, Nan Zhang, Vagelis Hristidis, Gautam Das

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Microblogging platforms such as Twitter have experienced a phenomenal growth of popularity in recent years, making them attractive platforms for research in diverse fields from computer science to sociology. However, most microblogging platforms impose strict access restrictions (e.g., API rate limits) that prevent scientists with limited resources-e.g., who cannot afford microblog-data-access subscriptions offered by GNIP et al.-to leverage the wealth of microblogs for analytics. For example, Twitter allows only 180 queries per 15 minutes, and its search API only returns tweets posted within the last week. In this paper, we consider a novel problem of estimating aggregate queries over microblogs, e.g., "how many users mentioned the word 'privacy' in 2013?". We propose novel solutions exploiting the user-timeline information that is publicly available in most microblogging platforms. Theoretical analysis and extensive real-world experiments over Twitter, Google+ and Tumblr confirm the effectiveness of our proposed techniques.

Original languageEnglish (US)
Title of host publicationSIGMOD 2014 - Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery
Number of pages12
ISBN (Print)9781450323765, 9781450323765
StatePublished - Jun 1 2014
Event2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014 - Snowbird, UT, United States
Duration: Jun 22 2014Jun 27 2014

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078


Other2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014
Country/TerritoryUnited States
CitySnowbird, UT

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems


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