Obamacare and Political Polarization on Twitter

Purposes

  • Classifying political ideology of Twitter users who talking about Obamacare.
  • Predicting Twitter users' political orientation using machine learning methods.
  • Preparing a dataset for social network analysis using political orientation labels.
  • Preparing a dataset of entire tweets (from Gardenhose) of the users who talked about Obamacare during the given period of time and labeling political orientation of the users for LIWC analysis.

Method

Network analysis in R with parallel programming and Tableau to deal with large size twitter data.

Role

Collaboration between School of Journalism and Mass Communication and Department of Statistics. Led a the sub-team to conduct data exploring and visualization by R and manage progress on Github.

Dingxian Cao
Dingxian Cao
Ph.D. Candidate in Econometrics

My research interests include High-dimensional Non-asymptotics and Uniform analysis of possible persistent Panel AR(1) model.