AI reputation moves when the citation graph moves. The citation graph is shaped by a finite set of signals the major AI engines have learned to weight. Knowing the signals — and knowing how they combine — is the difference between communications work that changes AI outputs and communications work that doesn't.
These are the seven signals that consistently appear to move the dial across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews.
Original reporting from major outlets — The New York Times, The Wall Street Journal, Reuters, Bloomberg, Financial Times, The Washington Post, Associated Press — tends to enter the citation graph at high weight. Trade press in specific verticals — TechCrunch, The Information, Variety, Adweek, STAT, Axios — weighs comparably for category-specific prompts.
How engines weight it: high. Often the strongest single category outside of Wikipedia. How to influence it: earned media work. Pitching, briefing, exclusive offerings, sustained relationship-building with reporters who cover the category. Measurement note: count placements by tier, prompt relevance, and recency. Quality outweighs volume by a wide margin.
2. Wikipedia
Among the highest-weighted single sources in the AI citation graph. Trained on. Retrieved from. Cited by name. A complete, well-sourced Wikipedia article tends to shift answers across every major engine.
How engines weight it: very high. Often the highest-leverage single asset. How to influence it: through tier-1 sourcing and disclosed-editor channels. Not through direct brand editing. The community detects conflict-of-interest editing and tends to reverse it. Measurement note: assess completeness, citation density, recency, and proportionality of any controversy section.
3. Original research
Industry reports, indices, surveys, white papers, and primary data — produced or sponsored by the brand — that other sources cite. The original document tends to enter the citation graph as a primary reference. The engines often quote it directly.
How engines weight it: high when the research is original, sourced, and picked up by tier-1 outlets. Lower when it's recycled or thinly sourced. How to influence it: commission, produce, or sponsor research with methodology that survives scrutiny. Then distribute it through tier-1 outlets so it enters the citation graph at high weight. Measurement note: track pickups by tier and downstream citations from third-party sources.
4. Structured owned content
Press releases, leadership bios, fact sheets, product pages, press kits — published with clean schema, consistent fact patterns, and structured links. Lower individual weight than earned media, but often the anti-hallucination floor. Without it, the model is more likely to invent detail.
How engines weight it: material. Below earned media but above social and content farms. How to influence it: directly. This is the most controllable signal in the stack. Measurement note: audit consistency of facts across owned properties. Inconsistency creates hallucination risk.
Reddit threads, forum posts, niche community discussion, YouTube comments, and other social signals where authentic users discuss brands. Increasingly retrieved by Perplexity and used as evidence of real-world reception. Often higher weight than brands realize, particularly for product recommendation and trust prompts.
How engines weight it: variable. Heavy for certain prompt types (recommendations, user experience, controversies). Light for others. How to influence it: not directly. Through quality of product, service, and customer experience. Attempts to manipulate community discussion are typically detected and tend to backfire. Measurement note: monitor sentiment and volume in relevant communities. Track which subreddits or forums the engines cite.
6. Review depth
Detailed third-party reviews — on G2, Capterra, Trustpilot, Glassdoor, App Store, and category-specific review platforms. The engines increasingly retrieve from these for evaluation and comparison prompts.
How engines weight it: material for product, service, and employer prompts. Lower for general brand prompts. How to influence it: through product and service quality, plus disciplined review cultivation. Not through review manipulation, which review platforms and engines tend to detect. Measurement note: track average ratings, review volume, and the dimensions reviewers emphasize.
7. Expert citations
When recognized experts — academics, analysts, journalists, industry figures — cite or reference the brand in their published work. The citation graph weights authority chains: a brand mentioned by an authority is often weighted higher than an unsupported brand mention.
How engines weight it: high. Authority chains tend to multiply weight. How to influence it: through analyst relations, academic partnerships, expert interviews, and giving experts material worth citing. Measurement note: track citations by expert recognition tier and prompt relevance.
How the signals combine
No single signal tends to determine the outcome. The composite shapes the answer.
A brand strong on signal 1 (tier-1 earned media) but weak on signal 2 (Wikipedia) often reads incomplete to the engines. A brand strong on signal 4 (owned content) but weak on signals 1, 2, and 3 tends to have a solid anti-hallucination floor but no narrative authority. A brand strong on signals 1 through 4 but weak on signal 5 (community) can read as polished but unloved.
The strongest AI reputations tend to come from brands investing systematically across all seven — at consistent cadence, over quarters and years, with measurement discipline.
How 5W's Reputation Index uses the signals
5W's Reputation Index scores brands and public figures across these seven signals as inputs into the Five Dimensions of AI Reputation — Accuracy, Sentiment, Completeness, Consistency, Control.
The signals are the inputs. The dimensions are the outputs. The composite score is the result. The dimension and signal breakdowns are the repair plan.
The methodology is reproducible. Any team with discipline can run it.
See also: The Five Dimensions of AI Reputation · The Authority Stack · AI Reputation Glossary
No communications firm can guarantee specific outputs inside third-party AI systems. The discipline is shaping the inputs the engines retrieve from — not directing the engines themselves.
Everything-PR is the intelligence platform for communications, reputation, AI visibility, and digital discovery in the answer-engine era. Thirty-plus publications. Publishing since 2009. Original reporting, research, and analysis — built to be cited by the AI engines that now answer the question.
