A small tool to view real-world ActivityPub objects as JSON! Enter a URL
or username from Mastodon or a similar service below, and we'll send a
request with
the right
Accept
header
to the server to view the underlying object.
{
"@context": [
"https://www.w3.org/ns/activitystreams",
{
"ostatus": "http://ostatus.org#",
"atomUri": "ostatus:atomUri",
"inReplyToAtomUri": "ostatus:inReplyToAtomUri",
"conversation": "ostatus:conversation",
"sensitive": "as:sensitive",
"toot": "http://joinmastodon.org/ns#",
"votersCount": "toot:votersCount",
"Hashtag": "as:Hashtag"
}
],
"id": "https://mastodon.social/users/katchwreck/statuses/109300003276375597",
"type": "Note",
"summary": null,
"inReplyTo": null,
"published": "2022-11-07T01:48:14Z",
"url": "https://mastodon.social/@katchwreck/109300003276375597",
"attributedTo": "https://mastodon.social/users/katchwreck",
"to": [
"https://www.w3.org/ns/activitystreams#Public"
],
"cc": [
"https://mastodon.social/users/katchwreck/followers"
],
"sensitive": false,
"atomUri": "https://mastodon.social/users/katchwreck/statuses/109300003276375597",
"inReplyToAtomUri": null,
"conversation": "tag:mastodon.social,2022-11-07:objectId=326629614:objectType=Conversation",
"content": "<p>my most recent publication is also my 1st sole-authored paper. it brings transparency to graph Laplacian methods by converting the eigenvector subspace to a topologically-informed probabilistic/fuzzy/soft embedding of the graph nodes. the source code is not available because i am working on commercial applications of this technique. if anyone has a graph/network analysis problem they want to try it on, please let me know! maybe we can develop a product for it :) <a href=\"https://mastodon.social/tags/ml\" class=\"mention hashtag\" rel=\"tag\">#<span>ml</span></a> </p><p><a href=\"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0204096\" target=\"_blank\" rel=\"nofollow noopener\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">journals.plos.org/plosone/arti</span><span class=\"invisible\">cle?id=10.1371/journal.pone.0204096</span></a></p>",
"contentMap": {
"en": "<p>my most recent publication is also my 1st sole-authored paper. it brings transparency to graph Laplacian methods by converting the eigenvector subspace to a topologically-informed probabilistic/fuzzy/soft embedding of the graph nodes. the source code is not available because i am working on commercial applications of this technique. if anyone has a graph/network analysis problem they want to try it on, please let me know! maybe we can develop a product for it :) <a href=\"https://mastodon.social/tags/ml\" class=\"mention hashtag\" rel=\"tag\">#<span>ml</span></a> </p><p><a href=\"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0204096\" target=\"_blank\" rel=\"nofollow noopener\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">journals.plos.org/plosone/arti</span><span class=\"invisible\">cle?id=10.1371/journal.pone.0204096</span></a></p>"
},
"updated": "2022-11-07T01:51:45Z",
"attachment": [],
"tag": [
{
"type": "Hashtag",
"href": "https://mastodon.social/tags/ml",
"name": "#ml"
}
],
"replies": {
"id": "https://mastodon.social/users/katchwreck/statuses/109300003276375597/replies",
"type": "Collection",
"first": {
"type": "CollectionPage",
"next": "https://mastodon.social/users/katchwreck/statuses/109300003276375597/replies?only_other_accounts=true&page=true",
"partOf": "https://mastodon.social/users/katchwreck/statuses/109300003276375597/replies",
"items": []
}
},
"likes": {
"id": "https://mastodon.social/users/katchwreck/statuses/109300003276375597/likes",
"type": "Collection",
"totalItems": 9
},
"shares": {
"id": "https://mastodon.social/users/katchwreck/statuses/109300003276375597/shares",
"type": "Collection",
"totalItems": 6
}
}