Public Demand for a More Informative Press


Andrew Trexler


WMP Post-Election Conference
2024 December 6

The media matters for democracy


Actual media coverage looks different


A classic perspective

Au contraire

  • Typical approaches to coverage do not broaden the audience
  • Instead, they double down on the most attentive: political hobbyists
  • Makes it more challenging to learn critical information
  • That is, typical approaches to covering politics are under-informative

An evolving news audience

Typical styles of coverage


Partisan Conflict

Insider Jargon

Prediction-as-News

Clickbait

Public interest

  • Centers normative expectations in the approach to coverage

Study Design

Prior evidence on demand

Prior evidence on learning

Field experiment

  • Partnered with Ground News
  • Recruited probability sample
  • Enrolled in email newsletter
    • 3x/week for 8 weeks
    • 5 headlines in each
  • Randomized headline styles
  • Track demand with digital trace data
  • Track learning with quiz surveys
  1. Who demands which styles?
  2. Are typical styles actually “under-informative”?
  3. Can public interest news broaden engagement?

Examples

Results

Engagement

Learning

Takeaways

  • Less political audiences want public interest news
  • Typical styles are under-informative
  • Potential avenues to grow news audiences


Thank you!

andrew.trexler@duke.edu
atrexler.com

Appendix

The new media environment

  • Fragmentation & specialization
  • Regular politics consumers are weird
  • Robust tools for tracking weird demand

  • Superficial news engagement
  • Mobile consumption
  • News “snacking”
  • Headlines have outsized importance

Shaping information value

  • Lots of decisions shape information value
    • Content itself
    • Placement, distribution
    • Style

Placement Choices

Placement Choices

Prioritizing Content

A big imbalance


  • Data: ~2M headlines published by major US outlets (2016-2020)
  • Train RAs to classify headlines
  • Build a training dataset
  • Train, fine-tune a semi-supervised machine learning model
  • DSL regression to estimate overall proportions

Shift to Mobile

Who seeks out political news?

Going After HVTs

  • Subscription revenue increasingly important for digital media
  • Intensive margin matters above extensive margin

Example decision task

Repackaging in any style


Coverage Style Example Headline
Public Interest Congress approves new military aid package for Ukraine in bipartisan votes
Partisan Conflict Congress approves new Ukraine funding, delivering Biden victory over GOP objections
Insider Jargon Johnson pushes through Ukraine aid bill despite objections from Freedom Caucus
Prediction-as-news The House Speaker’s push to approve new Ukraine funding might cost him his job
Clickbait Here’s how the House Speaker got around far-right opposition to secure Ukraine aid

Example vignette

Samples

  • Learning experiment
    • Nonprobability sample (Prolific)
    • Analysis sample n = 2,233
    • Fielded in 2023 September 21-22
  • Demand experiment
    • Nonprobability sample (Prolific)
    • Analysis sample n = 2,101
    • Fielded in 2024 April 26
  • Field experiment
    • Probability sample (registered voters)
    • Convenience sample (Ground News subscribers)
    • Analysis sample TBD, n ~ 1,500
    • Fielded 2024 July to October

Conjoint Sample

Vignette Sample

Measuring Political Engagement


  • “Subjective” measures
    • Attention to politics (0.125)
    • Interest in campaigns (0.125)
    • Days/week consuming political news (0.25)
  • “Objective” measures
    • Political knowledge items (0.5)

Back to: Conjoint

Back to: Vignette

Sample Distributions of Political Engagement


Conjoint Attributes

Attribute Levels
Headline Style Public Interest, Partisan Conflict, Insider Jargon, Prediction-as-news, Clickbait
News Topic/Story Economy (x4), Environment (x4), Foreign Policy (x4), Immigration (x4), Public Health (x4)
Outlet CNN, Fox News, New York Times, Politico, Wall Street Journal, Washington Post
Reading Time 1 minute read, 2 minute read, 3 minute read, 4 minute read

Predicted probabilities of selection

Source Outlet

Policy Issue Area

Reading Time

Apolitical Headlines

Nonlinear Interaction

Headline Evaluations

Learning Items

  • Texas bill
    • Allows state to remove local elected officials
      • \(\alpha = 3.54\), \(\beta = -0.38\)
    • Critics say undermines self-government
      • \(\alpha = 2.15\), \(\beta = -0.01\)
    • Affects Houston
      • \(\alpha = 1.63\), \(\beta = -0.69\)
    • Empowers appointee of governor
      • \(\alpha = 2.07\), \(\beta = -0.15\)
  • New York gerrymander
    • Governor supports redrawing
      • \(\alpha = 1.43\), \(\beta = -1.31\)
    • Current districts drawn for competitiveness
      • \(\alpha = 3.78\), \(\beta = 1.59\)
    • Current districts drawn by ind. court appointee
      • \(\alpha = 3.34\), \(\beta = 1.17\)
    • NY constitution bans gerrymandering
      • \(\alpha = 2.97\), \(\beta = -0.38\)
  • SCOTUS ethics bill
    • Strengthens gift reporting requirements
      • \(\alpha = 3.50\), \(\beta = -0.55\)
    • Scrutiny due to recent unreported gifts
      • \(\alpha = 7.89\), \(\beta = -0.85\)
    • Concern because gifts are very large
      • \(\alpha = 2.24\), \(\beta = -0.99\)
    • Bill applies to all justices
      • \(\alpha = 1.50\), \(\beta = 0.58\)

Back to Results

Replication

Variation by Style

Mechanical vs. Psychological Effects

Perceived Informativeness

Media Credibility

Support for Norm-breaking