Subnostr

We are going to be starting some work on this soon so we should be able to help provide data too.

#[10]

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.

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Discussion

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.

#[10]

Blake 2y ago

Worth also adding, this isn’t great for event flood attacks (like the hello in every language flood we saw), explicit media attacks (dick pic floods), or spam messages without a call to action - like perhaps you feel “GM” is spam - but who is the victim? Should be blocked more easily in client apps instead.

We will need a lot of smaller focused mechanisms to help limit bad actors and their content - however it will all work together and should create some solid results.