The impact of sampling on big data analysis of social media: A case study on flu and ebola

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

5 Scopus citations

Abstract

The explosive growth of online social networks in recent years have generated massive amount of data-sets in user behaviors, social graphs, and contents. Given the scale, heterogeneity, and diversity of such big data, sampling becomes a simple and intuitive approach to reduce the size of the data-sets for collecting, measuring, and understanding users, behaviors and traffic in online social networks. In this paper, we quantify the impact of random sampling on the analysis of online social networks with Twitter streaming data as a case study. In addition, we design different sampling strategies including community sampling and strata sampling, and evaluate their impact on a broad range of behavioral characteristics of online social networks. Our experimental results show that community sampling has the minimum impact on tweet distributions across users and the structure of retweeting graphs, while achieving the similar data reductions as random and stratified sampling.

Original languageEnglish (US)
Title of host publication2015 IEEE Global Communications Conference, GLOBECOM 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479959525
DOIs
StatePublished - 2015
Event58th IEEE Global Communications Conference, GLOBECOM 2015 - San Diego, United States
Duration: Dec 6 2015Dec 10 2015

Publication series

Name2015 IEEE Global Communications Conference, GLOBECOM 2015

Other

Other58th IEEE Global Communications Conference, GLOBECOM 2015
CountryUnited States
CitySan Diego
Period12/6/1512/10/15

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Communication

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