During this week, we will discuss theories concerning the existance of “echo chambers” online–being exposed only to information consistent with your existing political beliefs–and how researchers study this empirically.

Presentations

Emil & Lau are presenting:
How Sudden Censorship Can Increase Access to Information
William R. Hobbs and Margaret E. Robert (2018)

Readings

I would suggest focusing on the first two of these readings. We will implement the segregation measure from the first article in R in the lab.

  1. Ideological Segregation Online and Offline
    Quarterly Journal of Economics, 2011
    Matthew Gentzkow and Jesse M. Shapiro

  2. Avoiding the Echo Chamber about Echo Chambers
    Knight Foundation Report, 2018
    Andrew Guess, Benjamin Lyons, Brendan Nyhan, and Jason Reifler

  3. Filter Bubbles, Echo Chambers, and Online News Consumption
    Public Opinion Quarterly, 2016
    Seth Flaxman, Sharad Goel, and Justin M. Rao

There are also no echo chambers found with data from web browser data, or exposure in social media timelines (using the overlap coefficient as presented in the lab):

(Almost) Everything in Moderation: New Evidence on Americans’ Online Media Diets
American Journal of Political Science, 2021
Andrew M. Guess

Lecture

For reference, in the lecture I (will) refer to the following articles:

  1. Exposure to Ideologically Diverse News and Opinion on Facebook
    Science, 2015
    Eytan Bakshy, Solomon Messing, and Lada A. Adamic

  2. Selective Exposure in the Age of Social Media: Endorsements Trump Partisan Source Affiliation When Selecting News Online
    Communication Research, 2014
    Solomon Messing and Sean J. Westwood

  3. Exposure to Opposing Views Can Increase Political Polarization: Evidence from a Large-Scale Field Experiment on Social Media
    Proceedings of the National Academy of Sciences, 2018
    Christopher Bail, Lisa Argyle, Taylor Brown, John Bumpus, Haohan Chen, M. B. Fallin Hunzaker, Jaemin Lee, Marcus Mann, Friedolin Merhout, and Alexander Volfovsky

  4. Algorithmic Amplification of Politics on Twitter
    Proceedings of the National Academy of Sciences, 2022
    Ferenc Huszár, Sofia Ira Ktena, Conor O’Brien, Luca Belli, Andrew Schlaikjer, and Moritz Hardt

Lab

Data for labs

Members of Congress Twitter timelines: MOC_Tweets.rds

Ideology of news domains: Media_Scores.csv

Lab 1: Computing the isolation index

Lab reference code: Echo_Chambers.R

Note that there are 2 errors in the code presented in the video, which are now fixed in the R file:

  1. On lines 117-123, the denominator should be lib_m (not cons_m)
  2. The nytimes.com and cnn.com were incorrectly ordered in the code leading to their ideology estimates being swapped.

Lab 2: Computing the overlap coefficient

Lab reference code: Echo_Chambers_Overlap.R