Rhea: Adaptively sampling authoritative content from social activity streams

Panagiotis Liakos, Alexandrosb Ntoulas, Alex Delis

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

Abstract

Processing the full activity stream of a social network in real time is oftentimes prohibitive in terms of both storage and computational cost. One way to work around this problem is to take a sample of the social activity and use this sample to feed into applications such as content recommendation, opinion mining, or sentiment analysis. In this paper, we study the problem of extracting samples of authoritative content from a social activity stream. Specifically, we propose an adaptive stream sampling approach, termed Rhea, that processes a stream of social activity in real-time and samples the content of users that are more likely to provide influential information. To the best of our knowledge, Rhea is the first algorithm that dynamically adapts over time to account for evolving trends in the activity stream. Thus, we are able to capture high quality content from emerging users that contemporary white-list based methods ignore. We evaluate Rhea using two popular social networks reaching up to half a billion posts. Our results show that we significantly outperform previously proposed methods in terms of both recall and precision, while also offering remarkably more accurate ranking.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages686-695
Number of pages10
ISBN (Electronic)9781538627143
DOIs
StatePublished - Jul 1 2017
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
CountryUnited States
CityBoston
Period12/11/1712/14/17

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

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