What is Honest Headlines?

Honest Headlines is a news comparison tool that aggregates partisan media coverage, separates facts from opinions, and scores relative accuracy so readers can compare framing without reading only one side.

How do you score accuracy?

Scores estimate how checkable and specific claims appear in available headlines and summaries—numbers, attribution, dates, and quotes raise accuracy; hedges and loaded language lower it. Scores are not definitive truth verdicts.

How is composite trust calculated?

For each source or author we average four 0–100 metrics across stored articles, then compute 35% accuracy + 30% fact ratio + 20% × (100 − sensationalism) + 15% × (100 − opinion ratio), rounded to 0–100. Higher trust means more checkable, less loaded framing in available text—not a permanent verdict or guarantee of truth.

What is the Trust page?

Trust ranks sources or authors from durable article history using fact, opinion, accuracy, and sensationalism averages. Sort by Trust (source) or Trust (author) for a composite score, or by Accuracy, Facts, Opinion, or Sensationalism to rank by that metric alone.

Is the app biased toward one party?

The product is designed for side-by-side comparison. Source lean labels reflect commonly reported audience tilt, not formal party affiliation.

Where do articles come from?

Public RSS feeds from outlets across the political spectrum. Only recent items are shown so the feed stays current.

Mission · media trust · methodology map

About Honest Headlines: data for media trust and reliability

We help readers compare left and right coverage and inspect trustworthiness signals for sources and authors—using transparent metrics, not a single partisan feed.

Trust in news is strained: the same event can be framed differently across outlets, and audiences often lack practical tools to compare framing without drowning in tabs. Honest Headlines exists to narrow that gap with live aggregation, fact vs opinion and accuracy scores, sensationalism cues, quote comparison, bias memory, durable history, and ranked trust for sources and authors.

This About page explains the purpose of the site, how each page fits the mission, and the methodologies behind the numbers. Scores estimate checkability and loaded language in available text—they are insight for media literacy, not final truth verdicts. Always open original articles.

Built for a low-trust media environment

Many people no longer assume that a single network, paper, or feed is enough to understand public events. Coverage of the same story can diverge in emphasis, sourcing, quotation, and emotional heat. That gap feeds polarization and cynicism—even when the underlying record is shared.

Honest Headlines is built to help address that lack of trust—not by crowning “the truth,” but by making framing visible and comparable. We aggregate live and historical signals so you can judge how checkable language looks, how loaded the presentation is, and how sources and authors behave over time. The goal is insight into trustworthiness and reliability using data and metrics, always paired with links back to original reporting.

Metrics estimate checkability and framing from headlines and summaries (and optional deeper analysis). They are tools for media literacy—not courtroom findings, party ratings, or a substitute for reading the source.

Core metrics and methods

Fact vs opinion

Fact-leaning cues include numbers, dates, attribution, institutions, and quotes. Opinion-leaning cues include prescriptions, tribal labels, and evaluative heat. Ratios sum to 100 for the scanned text.

Accuracy (checkability)

Higher when claims are specific and attributed; lower when hedges, anonymous framing, or pure evaluation dominate. Not a verdict on whether an event happened.

Sensationalism

Rises with clickbait patterns, combat language, and emotional load relative to dry wire-style presentation. High sensationalism is not the same as “false.”

Composite trust

On Trust, entity averages combine 35% accuracy + 30% fact ratio + 20% × (100 − sensationalism) + 15% × (100 − opinion). Sort by a single metric when you want that axis alone.

Quote comparison lines up attributed remarks across sides and flags selective shortening, missing context, loaded paraphrase, and emphasis shifts in the available text. Overlapping topics require coverage on both sides with ≥25% keyword overlap. Bias memory stores rolling twelve-month source/author rollups; article history stores individual scored stories for search and long-term review.

How the pages fit together

Each surface answers a different question. Use them together: live scan → same-story compare → source/author patterns → ranked trust → archive and search.

Principles

Side by side — show both partisan ecosystems rather than a single feed bubble.
Transparent signals — metrics are explained on Trust, FAQs, and page intros; lean labels are audience tilt, not party membership.
Original sources first — every card and history row links out; scores never replace reading the article.
Humility about limits — RSS snippets and heuristics miss full-body context; optional AI analysis is still not omniscient.

Honest Headlinesis not affiliated with any political party or news organization. For methodology Q&A, see FAQs. To start comparing today’s cycle, open the live dashboard.