Estimating the Ideology of Political YouTube Videos

Angela Lai, Megan A. Brown, James Bisbee, Joshua A. Tucker, Jonathan Nagler, Richard Bonneau

Research output: Contribution to journalArticlepeer-review


We present a method for estimating the ideology of political YouTube videos. The subfield of estimating ideology as a latent variable has often focused on traditional actors such as legislators, while more recent work has used social media data to estimate the ideology of ordinary users, political elites, and media sources. We build on this work to estimate the ideology of a political YouTube video. First, we start with a matrix of political Reddit posts linking to YouTube videos and apply correspondence analysis to place those videos in an ideological space. Second, we train a language model with those estimated ideologies as training labels, enabling us to estimate the ideologies of videos not posted on Reddit. These predicted ideologies are then validated against human labels. We demonstrate the utility of this method by applying it to the watch histories of survey respondents to evaluate the prevalence of echo chambers on YouTube in addition to the association between video ideology and viewer engagement. Our approach gives video-level scores based only on supplied text metadata, is scalable, and can be easily adjusted to account for changes in the ideological landscape.

Original languageEnglish (US)
JournalPolitical Analysis
StateAccepted/In press - 2024


  • ideology estimation
  • latent variable
  • YouTube

ASJC Scopus subject areas

  • Sociology and Political Science
  • Political Science and International Relations


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