#tfidf
Live, measured metrics for the hashtag #tfidf from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #tfidf
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · mastodon.social (Mastodon public tags API) · fetched 2026-10-19 04:22 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mastodon.social (Mastodon public search API) · fetched 2026-10-19 04:22 UTCLive pulse
measured · mastodon.social (Mastodon tag timeline) · fetched 2026-10-19 04:22 UTCEverything below is measured over the latest 34 public posts (spanning ~58150 hours).
Posting hours (UTC) — busiest: 13:00
Languages: English (14) · Russian (13) · Polish (4) · Japanese (2) · Chinese (1)
Avg boosts / post: 0.4
Top of the latest posts
Okay, Back of the napkin math: - There are probably 100 million sites and 1.5 billion pages worth indexing in a #search engine - It takes about 1TB to #index 30 million pages. - We only care about text on a page. I define a page as worth in
Lazy-fedi-question... I have a "working"(?) code example of TF-IDF #tfidf using #scikitlearn and I know the main concepts, but all the tutorials I find are a bit — I don't want to be harsh but —crappy... Can someone point me to some nice op
Recently I've combined various functions which I've been using in other projects (e.g. my personal PKM toolchain) and published them as new library https://thi.ng/text-analysis for better re-use: - customizable, composable & extensible toke
What “tfidf” means
WikipediaIn information retrieval, tf–idf is a measure of importance of a word to a document in a collection or corpus, adjusted for the fact that some words appear more frequently in general. Like the bag-of-words model, it models a document as a multiset of words, without word order. It is a refinement over the simple bag-of-words model, by allowing the weight of words to depend on the rest of the corpus.
“Tf–idf” on Wikipedia (CC BY-SA) →#tfidf across platforms
every network with a public tag surfaceFollow #tfidf straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/tfidf