A filter meant to drop sponsored social-media posts used word-boundary patterns such as \bpartner\b and #[a-z0-9]*partner\b. Platform-mandated disclosure tags are often written with an underscore (#brandname_partner). In regex, _ is a word character, so there is no \b between "_" and "partner", and [a-z0-9]* stops at the underscore: the tag is never matched. On one brand, these posts grew from 21 % to 51 % of captions while the "sponsored share" stayed flat, so a paid campaign looked like organic growth.

When validating a social-media signal (per-entity mention growth year over year) with a "more distinct authors, not just more posts" robustness check, normalizing an entity's distinct-author count by the number of distinct authors who mentioned ANY entity looks natural but fails badly when the collection changed between periods. After a crawl that captured fewer posts per author, every entity lost roughly 44 % of its distinct authors while the pool of authors naming any entity barely moved (-5 %), so 86 % of genuinely rising entities failed the check.

When ranking named entities (brands, products) by growth in social-media captions, two silent failure modes inflate or deflate results. (1) A pooled denominator ("captions naming any entity per week") is contaminated when one entity name is reused as a meme/spam hashtag: one name produced 1.2M captions in a single summer on unrelated funny videos, inflating the pool and making most other entities look like they grew. (2) Phrases that pass dictionary filters still mean something else in the scoring window: "on cloud" was 65% "on cloud 9" (a concert tour), "caesars" 78% a pizza chain, a surname became a politician in the news, a brand name was Honduran slang, a town name. Aggregate tests (unit tests, matcher diffs) do not catch these.

A word-bounded phrase matcher (Aho-Corasick or regex) over social-media captions misses brands glued into hashtags (#brandhaul, #brandsandals), plurals, curly apostrophes from phone keyboards (brand’s), unaccented spellings, and 3-letter names dropped by a minimum-length filter. The naive fixes (match any hashtag that starts with the brand, fold accents in captions) create large false-positive counts.

Ranking brands by "mentions per 10k videos" from a scraped social-media dump gave every brand a ×3–5 jump in the last weeks of the crawl and non-domestic brands ×10–30 year-over-year. The crawl's country mix and its late-period sampling changed (the share of domestic videos fell from ~87% to ~25% and the surviving domestic videos named a brand five times more often), so a per-video denominator inflated everyone at once and the ranking measured the crawler, not the brands.