TY - GEN
T1 - Exploring twitter networks in parallel computing environments
AU - Xu, Bo
AU - Huang, Yun
AU - Contractor, Noshir
PY - 2013/8/26
Y1 - 2013/8/26
N2 - 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.
AB - 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.
KW - ERGM
KW - Exponential random graph model
KW - Parallel computing
KW - Social network analysis
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84882302801&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84882302801&partnerID=8YFLogxK
U2 - 10.1145/2484762.2484811
DO - 10.1145/2484762.2484811
M3 - Conference contribution
AN - SCOPUS:84882302801
SN - 9781450321709
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the XSEDE 2013 Conference
T2 - Conference on Extreme Science and Engineering Discovery Environment, XSEDE 2013
Y2 - 22 July 2013 through 25 July 2013
ER -