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 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.