Exploring twitter networks in parallel computing environments

Bo Xu, Yun Huang, Noshir Contractor

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

3 Scopus citations

Abstract

Millions of users follow each other on Twitter and form a large and complex network. The size of the network creates statistical and computational challenges on exploring and examining individual behavior on Twitter. Using a sample of 697,628 Korean Twitter users and 34 million relations, this study investigates the patterns of unfollow behavior on Twitter, i.e. people removing others from their Twitter follow lists. We use Exponential Random Graph Models (p*/ERGMs) and Statnet in R to examine the impacts of reciprocity, status, embeddedness, homophily, and informativeness on tie dissolution. We perform data processing, statistics calculation, network sampling, and Markov chain Monte Carlo (MCMC) simulation on Gordon, a unique supercomputer at the San Diego Supercomputer Center (SDSC). The process demonstrates the role of advanced computing technologies in social science studies.

Original languageEnglish (US)
Title of host publicationProceedings of the XSEDE 2013 Conference
Subtitle of host publicationGateway to Discovery
DOIs
StatePublished - Aug 26 2013
EventConference on Extreme Science and Engineering Discovery Environment, XSEDE 2013 - San Diego, CA, United States
Duration: Jul 22 2013Jul 25 2013

Publication series

NameACM International Conference Proceeding Series

Other

OtherConference on Extreme Science and Engineering Discovery Environment, XSEDE 2013
CountryUnited States
CitySan Diego, CA
Period7/22/137/25/13

Keywords

  • ERGM
  • Exponential random graph model
  • Parallel computing
  • Social network analysis
  • Twitter

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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