FrameCheck
An AI reading companion that, when you paste a news link, surfaces the dominant interpretive frame (power-politics, market, ritual, human-interest, security) and shows how other outlets covered the same event.
News-literate readers, journalism students, and educators who want to compare how different publications frame the same international event
- One-click frame breakdown: paste a link and get a labeled card (e.g., 'Power-politics 60%, Ritual 15%, Market 10%, Other 15%') with the sentences supporting each label
- Cross-outlet comparison view: paste up to 5 URLs about the same event and see a side-by-side frame matrix so you can spot which outlets skip the human angle entirely
- Reading-mode export that strips ad-jargon and writes a 'what this article is actually saying' summary for students new to foreign-affairs coverage
- Saved-frame library so journalism teachers can build a weekly 'frame of the week' lesson from current events
The Dawn op-ed crystallizing journalism's blind spot on Iranian funeral coverage is a teaching moment: readers increasingly want a tool that shows them which questions a story is and isn't asking.
News-literacy audience exists (educators, journalism students, civics-curious readers) but the specific framing-typology angle is a niche subset of media-bias interest; Ground News confirmed live but broader demand signals couldn't be verified.Breaking News Headlines Today | Ground News ↗
Ground News owns the bias-comparison niche and Ad Fontes dominates bias+quality scoring; the specific interpretive-frame taxonomy (Entman: power-politics, market, ritual, human-interest, security) is a real but narrow whitespace adjacent to those players.Breaking News Headlines Today | Ground News ↗
Ground News and Ad Fontes prove B2C willingness to pay for bias tools, but education budgets are tight and free LLM alternatives (e.g., 'paste article into ChatGPT') compress pricing power.Breaking News Headlines Today | Ground News ↗
Media literacy is durable, but the underlying capability (classify a frame, summarize coverage across outlets) is exactly what general-purpose assistants are absorbing; defensibility is weak once OpenAI/Perplexity ship native framing views.
MVP is a weekend build: prompt an LLM with the Entman taxonomy, aggregate 5–10 outlet RSS feeds per event, render side-by-side frames; the hard part is multi-outlet coverage collection and framing-consistency evaluation, not the AI itself.