Subnostr

Nice.

Spam definitely is tough to define. When I made the dataset I mostly used the definitions of “is this content intended to deceive” (is there a victim?) or “is this promotional in an excessive way” (basically like Adblock). This means that explicit content or strong opinions and other content wasn’t labelled as spam.

One example I saw a lot of with kind=1984 was “marked as spam because foreign language”. I translated a lot of content that certainly wouldn’t fall under those above definitions - it was more a form of censorship.

There will never be a common standard or definition for Nostr spam that suits everyone - however something closer to Adblock and your spam email inbox is where I see this being most useful. Occasional false positive, however it’s not a big deal - especially when you can whitelist people as contacts.

Blake 2y ago

Generally below is what I used as definitions.

The issue with nsfw is it’s more of a broader label, than “spam or not spam”. Certainly you could build a model with more categories or even multi-category.

The secondary issue is nsfw is mostly visual (you can just have a client app setting to mask swear/adult words) - and using event content that is minimal text and url/s, would train really poorly, without some image/media classifier too. Certainly a service that could exist too (hotdog or not hotdog).

Impersonation often has overlap with spam, due to the dodgy call to action - however I haven’t targeted it directly. An example where impersonation could be ok is parity accounts or satire. I think it’s more a verification problem and less suited for text labelling.

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