Tone of presidential coverage across major US broadcast networks and digital news outlets, 2025–present.
% positive minus % negative each week. Faint lines are raw weekly values; bold lines are LOESS-smoothed (span = 0.5) with 95% CI.
Share of segments/headlines classified as negative each week.
Total segments or headlines mentioning the president each week.
Mean net score across the selected date range, by network.
Share of weekly TV segments mentioning each topic. Click chips below to add or remove topics from the chart. Topics are matched by keyword (case-insensitive, whole-word) — definitions live in scripts/topics.yaml.
The Presidential Coverage Tracker is a research project from Yale political scientists that measures the tone of presidential coverage across major US broadcast and cable news networks and digital news outlets. We collect closed-caption transcripts from the Internet Archive's TV News Archive and news headlines from Media Cloud, classify each segment and headline mentioning the president using a fine-tuned natural language inference model, and update this dashboard every few days.
The classification model, training data, and analysis code for the net coverage score are open source. The paper's code repository is at github.com/kevin-deluca-polisci/presidential_headlines; the code for this dashboard lives at github.com/kevin-deluca-polisci/coverage-tracker. The full paper is forthcoming.
Traditional methods of measuring media tone — dictionary-based word counts and document-level transformer sentiment classifiers — assign an overall positive, negative, or neutral score to a document rather than to the candidate it discusses. This document-level approach cannot identify to whom sentiment is directed. A headline like "Biden struggles to contain inflation" conveys a different signal about the president than "Inflation eases under Biden," yet both might be flagged as negative by a dictionary model that treats "inflation" as a negative term. We adapt methods from natural language inference (NLI) to measure candidate-specific media coverage. Rather than inferring tone from surface-level word usage, our approach uses stance detection to evaluate whether a headline or transcript segment implies that a given candidate is performing well or poorly.
Each broadcast transcript is split into 3-sentence windows. Windows mentioning "Trump" are classified using the Political DEBATE model developed by Michael Burnham. We further train the model on a task that evaluates whether the text conveys a positive, negative, or neutral performance cue about Trump; full training procedure, validation, and reliability tests are documented in our code repository and accompanying paper. The same method applies to digital news coverage using the headline as the unit of analysis.
A segment is classified as positive if the model scores a "yes" on the hypothesis "the author of this text believes that Trump is performing/performed/will perform well" and negative if the model scores a "yes" on the parallel "poorly" hypothesis.
The bold lines in each over-time chart are LOESS smooths (span = 0.5) fit to the weekly aggregates. Shaded bands are 95% confidence intervals from the LOESS fit. Faint lines underneath show the underlying weekly raw values that the smoother is fit to.
Broadcast and cable: CBS, CNN, Fox News, ABC, NBC, and MSNBC/MSNow. Data runs from January 2025 to the present. We include national broadcast and cable news programs only — no local affiliates or regional newscasts.
Digital news: Reuters, Fox News, CBS News, Bloomberg, CNN, ABC News, USA Today, New York Times, NBC News, Los Angeles Times, and NPR. Headlines are collected via the Media Cloud API.
The weekly aggregated CSVs that power this page are released under CC BY 4.0.
Kevin DeLuca · kevin.deluca@yale.edu
Zoe Kava · zoe.kava@yale.edu