August 9, 2019

Open materials

Problem

  • Lots of talk about “echo chambers”
    • Surely this is a continuous phenomenon
    • Segregation a clearer concept
  • Segregation in this context = different media diets by partisanship
  • Related to but distinct from selective exposure theory
    • Interest in how much segregation, not why there is segregation
  • How do you measure this?

What I am presenting

  • Using network analysis to approach this problem
    • Residential (racial) segregation
  • Focusing on people as unit of analysis
    • Rather than sources or audiences
  • Goal: a number that could be plugged into a regression model

What I am presenting

A measure that…

  • Can describe people
    • Person X has Y level of segregation
  • Accommodates multi-party systems
  • Is comparable across contexts
    • parties, countries, non-media contexts
  • Requires no more complex designs than usual

Past work

Selective exposure research

  • Control for partisanship, ideology, congenial media use
  • No preference/positive preference for out-party media
  • Not a measure of the information environment!

Past work

Tracking data (e.g., Bakshy, Messing, & Adamic, 2015)

  • Define sources as liberal/neutral/conservative based on who tends to click/share
  • How much liberal media on conservatives’ news feeds, vice versa?
    • About 25%

Past work

Tracking data

  • Gentzkow & Shapiro (2011) have individual-level web and TV tracking data
    • Dichotomize sources
    • True ideology unknown for many respondents
    • Minimal “segregation” (= .08 on a measure bounded at 0 and 1)
    • Similar design and results from Flaxman & Goel (2016)

Spectral Segregation Index (SSI)

  • Developed to measure residential segregation (Echenique & Fryer, 2007)
  • Does not impose binary classification
  • Produces measurement at individual, group (party), and system levels
  • Can use for people or sources
  • Single number bounded approx. at -1 (active avoidance of in-group) and 1 (perfect segregation)

Spectral Segregation Index (SSI)

  • Requires a network/graph representation

For people:

  • Nodes are people, edges reflect co-consumption
  • Weight of edges = # of shared sources

For sources:

  • Nodes are sources, edges reflect co-consumers
  • Weight of edges = # of pairs of people who both use source

Data

Pew Research Center’s American Trends Panel, Wave 1 (2014)

  • N = 3,308
  • Represenative of adults in USA
  • 66 political media sources
    • Mix of TV, internet, print, radio (some overlapping)
    • Did you get news from in the past week? (Yes/No)
    • About 5 sources per respondent
  • Sources coded as left/Democrat-favoring, right/Republican-favoring, or non-partisan
  • Respondents self-report partisanship