Sentiment and context of AI citations

It's tempting to treat 'we got cited' as a win, but the context matters enormously. An engine might cite you as an example of what not to do, describe you with outdated information, or name you in a comparison where you come off worst. A citation embedded in a negative or inaccurate framing can hurt more than help. So sentiment and context is a metric distinct from raw citation frequency.

To track this, you have to read the actual answers you appear in - run your target questions and examine how you're described, not just whether you're named. Look for accuracy (are the facts about you right?), sentiment (favorable, neutral, negative?), and comparative framing (how do you stack up in 'X vs Y' answers?). This qualitative review is the only way to catch misrepresentation.

Unfavorable or inaccurate portrayal usually has a fixable cause: outdated information the engine learned from stale content, unclear content that led to a wrong inference, or an absence of good content that let a worse source define you. Diagnosing which lets you fix the root - update stale facts, clarify confusing content, or publish the accurate answer that should define you.

You can't directly edit an engine's answer, but you strongly influence it through the content and web signals it draws on. Accurate, current, clearly-written content - and corroboration across the web - is how you shape a more favorable, accurate portrayal over time. This is the same authority-and-clarity work as the rest of GEO, applied specifically to fixing how you're described.