Analysis of Twitter data with the Bayesian fused graphical lasso

Mehran Aflakparast, Mathisca de Gunst and Wessel van Wieringen

PLOS ONE, 2020, vol. 15, issue 7, 1-28

Abstract: We propose a method to simplify textual Twitter data into understandable networks of terms that can signify important events and their possible changes over time. The method allows for common characteristics of the networks across time periods and each period can comprise multiple unknown sub-networks. The networks are described by Gaussian graphical models and their parameter values are estimated through a Bayesian approach with a fused lasso-type prior on the precision matrices of the underlying mixtures of the sub-models. A flexible data allocation scheme is at the heart of an MCMC algorithm to recover mean and covariance parameters of the mixture components. Several implementations of the outlined estimation procedure are studied and compared based on simulated data. The procedure with the highest predictive power is used for mining tweets regarding the 2009 Iranian presidential election.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235596 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 35596&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0235596

DOI: 10.1371/journal.pone.0235596

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().