#cooccurrence
Live, measured metrics for the hashtag #cooccurrence from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #cooccurrence
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 · fosstodon.org (Mastodon public tags API) · fetched 2026-07-30 00:23 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-07-30 00:23 UTCNo related tags with measured usage found for #cooccurrence.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-30 00:23 UTCEverything below is measured over the latest 4 public posts (spanning ~7173 hours).
Posting hours (UTC)
Languages: English (4)
Avg boosts / post: 2.8
Top of the latest posts
"You shall know a word by the company it keeps." Distributional Semantics as the basis for self supervised learning as in today's large language models. This is what my colleague @MahsaVafaie and I are going to talk about in this #kg2023 le
Very happy to be in #Montpellier today for a "journée d'étude" on the notion of #tupleization in the context of #cooccurrence, #keyness, #frequency and #dispersion. – The opening speaker is Stefan Th. Gries, and the full programme can be fo
I have a hard time finding a #Python implementation of statistical #cooccurrence/#collocation tests like log-likelihood (as described by Dunning 1993). There’s an #RStats implementation in #PolmineR, but isn’t there any for Python? Any hint
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/cooccurrence