My research program

• Relationship between new media and news media  

• Civic engagement and political polarization  

• Quantitative research methods and data science  

Publications

• Political use of social media
  • Latent variable analysis of political interactions on Facebook (Communication Research Reports, 2017)
  • Field experiment examining effect of following candidates [on Facebook] on civic engagement (co-author, Computers in Human Behavior, 2015)
• Quantitative methods in communication research
  • Reviews of categorical (Sage Encylopedia) , longitudinal (Sage Encylopedia) , and new media-leveraging methods (Handbook of Political Advertising, 2016)

Partisan media

• Media fragmentation poses several problems
  • User-level agenda setting lacks regulation (Garrett, 2013) e.g., fake news (Stanford History Education Group, 2016), and new currency is social validation (Messing & Westwood, 2012)
  • Digital partisans in social media echo chambers (Barbera, et al., 2015; Himelboim, Smith, & Schneiderman, 2013)
• Concerns compounded by methodological problems
  • Sampling, reliability, and elusive promise of "big data" (Garrett, 2013)

Partisan networks

• We know a lot about exposure to partisan news
  • e.g., theories of motivated reasoning and cognitive dissonance (Prior, 2013)
• We know relatively little about partisan [user] networks
  • We lack necessary tools to examine networks and interactions on a large scale (Garrett, 2013)
  • This was my motivation for writing rtweet an R package for collecting Twitter data (Kearney, 2016)

Selective exposure on Twitter

• Asymmetrical connections make Twitter unique
  • Estimate political ideology using user networks (Barbera, 2015)
• My dissertation
  • I tracked networks from a random sample of Twitter users during the 2016 election

Hypotheses

1. User networks on Twitter will cluster according to partisanship
   • Partisan network homogeneity
2. Network polarization will increase with proximity to election
   • Change in partisan network homogeneity

Method

1. Identified every follower from 12 source, or origin, accounts representing 3 groups
2. Randomly sampled 10,000 followers from each group
3. Filtered out inactive/bot-like users for final sample of 3,000  
   • Democrat partisans (n = 1,000)
   • Republican partisans (n = 1,000)
   • Entertainment non-partisans (n = 1,000)

Partisan clusters (H1)

• If partisanship organizes networks, visual evidence in connections should be clear
• For reference, I simulated data using two sets of parameters (null vs alternative model)

Change in polarization (H2)

• If partisan politics amplifies partisanship, then network polarization should increase as the election gets closer
• My dissertation compares within-user change between partisan and non-partisan groups over time using multilevel modeling

Underlying research question

• How do people navigate political information in new media environments?
• In what ways do digital networks operate as mechanisms of political persuasion?
• What do patterns in social media behaviors of tell us about public opinion?

Dissertation, Twitter, and rtweet

• rtweet: Collecting Twitter data (R Journal; ICA)
• Change in network polarization on Twitter (Journal of Communication)
• Topic salience in Presidential debates (Political Analysis)
• Circulation of NYTimes stories about sanctions (Political Communication)

Other projects

• Combining real-time Facebook API data with survey data
• Combining social media or tracking data with experimental data to examine framing, social validation, and media exposure
• Working with databases, parsing text, analyzing multiple models, and leveraging other programming languages to make things go faster and do more.