Answer absorption is distinct from citation selection. Citation selection is when an AI system lists a source in its citations. Absorption is when that source actually shapes the words and claims in the AI's generated answer — a more direct and influential form of AI engagement.

This distinction is established in Zhang et al. (2026), 'From Citation Selection to Citation Absorption', a preprint study analysing 21,143 citations across ChatGPT, Google AI Overviews, and Perplexity. The study found that high-influence pages — those whose content was visibly absorbed into AI answers — shared specific structural and content properties: they were significantly longer, had far more headings and paragraphs, and were more likely to contain definitional and comparative language.

This tool operationalises those structural signals. It does not predict whether any specific piece of content will be absorbed by any specific AI system at any specific time. It measures the structural properties associated with higher absorption probability.

Word count, headings, paragraphs (structural dimensions):

Word count: Word count tiers are calibrated from Zhang et al. (2026, preprint — not yet peer-reviewed), which found high-influence pages were on average 11.44× longer than low-influence pages. The specific word count thresholds are the tool's internal calibration.

Headings: Heading count tiers are calibrated from Zhang et al. (2026, preprint — not yet peer-reviewed), which found high-influence pages had 12.50× more headings. Specific tier thresholds are the tool's internal calibration.

Paragraphs: Paragraph count tiers are calibrated from Zhang et al. (2026, preprint — not yet peer-reviewed), which found high-influence pages had 5.69× more paragraphs. Specific tier thresholds are the tool's internal calibration.

Definition sentences:

Definition sentence detection is based on Zhang et al. (2026, preprint — not yet peer-reviewed), which found pages with high definitional content showed approximately 57% higher absorption. This is a page-level finding applied as a document-level signal — an informed inference, not a directly measured sentence-level effect.

Comparative sentences:

Comparative sentence detection is based on Zhang et al. (2026, preprint — not yet peer-reviewed), which found comparative content was associated with approximately 55% higher absorption. Page-level finding applied as a document-level signal.

Statistics presence:

Statistics presence is associated with approximately 61% higher absorption in Zhang et al. (2026, preprint — not yet peer-reviewed). Note: a separate peer-reviewed study (Aggarwal et al. 2024) found approximately +31% for citation selection — this is a related but distinct phenomenon.