Due to the recent spam waves affecting the Fediverse, we’d like to open requests for comment on the use of automated moderation tools across Pawb.Social services.
We have a few ideas on what we’d like to do, but want to make sure users would feel comfortable with this before we go ahead with anything.
For each of these, please let us know if you believe each use-case is acceptable or not acceptable in your opinion, and if you feel like sharing additional info, we’d appreciate it.
1. Monitoring of Public Streaming Feed
We would like to set up a bot that monitors the public feed (all posts with Public visibility that appears in the Federated timeline) to flag any posts that meet our internally defined heuristic rules.
Flagged posts would be reported per normal from a special system-user account, but reports would not be forwarded to remote instances to avoid false-positives.
These rules would be fixed based on metadata from the posts (account indicators, mentions, links, etc.), but not per-se the content of the posts themselves.
2. Building of a local AI spam-detection model
Taking this a step further, we would like to experiment with using TensorFlow Lite and Google Coral Edge TPUs to make a fully local model, trained on the existing decisions made by our moderation team. To stress, the model would be local only and would not share data with any third party, or service.
This model would analyze the contents of the post for known spam-style content and identifiers, and raise a report to the moderation team where it exceeds a given threshold.
However, we do recognize that this would result in us processing posts from remote instances and users, so we would commit to not using any remote posts for training unless they are identified as spam by our moderators.
3. Use of local posts for non-spam training
If we see support with #2, we’d also like to request permission from users on a voluntary basis to provide as “ham” (or non-spam / known good posts) to the spam-detection model.
While new posts would be run through the model, they would not be used for training unless you give us explicit permission to use them in that manner.
I’m hoping this method will allow users who feel comfortable with this to assist in development of the model, while not compelling anyone to provide permission where they dislike or are uncomfortable with the use of their data for AI training.
4. Temporarily limiting suspected spam accounts
If our heuristics and / or AI detection identify a significant risk or pattern of spammy behavior, we would like to be able to temporarily hide / suppress content from the offending account until a moderator is able to review it. We’ve also suggested an alternative idea to Glitch-SOC, the fork we run for furry.engineer and pawb.fun, to allow hiding a post until it can be reviewed.
Limiting the account would prevent anyone not following them from seeing posts or mentions by them, until their account restriction is lifted by a moderator.
In a false-positive scenario, an innocent user may not have their posts or replies seen by a user on furry.engineer / pawb.fun until their account restriction is lifted which may break existing conversations or prevent new ones.
We’ll be leaving this Request for Comment open-ended to allow for evolving opinions over time, but are looking for initial feedback within the next few days for Idea #1, and before the end of the week for ideas #2 through #4.
#2 seems to require #3 by definition – the model can’t know what spam is without knowing what ham is as well. In general a DSpam model would seem to be the right one – all posts used to train ham, individual posts marked as spam are removed from the ham set and added to the spam set, and then a separate spam feed that could be monitored for false positives.
In general all of these approaches sound fine to me – I hope that mastodon can develop a built-in spam suppression system but for now we have to rely on these bespoke approaches.