Discourse Disorder

:warning: This feature is now part of Discourse AI. This plugin is deprecated.

:discourse2: Summary Disorder helps moderators by automatically flagging potential toxic content on your Discourse forum.
:hammer_and_wrench: Repository Link https://github.com/xfalcox/disorder
:open_book: Install Guide How to install plugins in Discourse

If you are using our official hosting service, please contact our support to register your interest for this plugin.


As @hawk said in Dealing with Toxicity in Online Communities, managing toxicity in your community is fundamental.

While Discourse ships out of the box with many different tools to help manage toxicity in your community, we are always researching ways to improve it further. In particular, I’ve been researching about possible applications of Machine Learning and AI in online forums. Which is now an experimental plugin, available to all communities.

The plugin

Disorder leverages Artificial Intelligence and Machine Learning to help you moderate you community, making it easier for your moderation team to be on top of potentially problematic content and even optionally nudging your users to revise toxic posts before posting.

This is a first foray into using self-hosted ML models in Discourse, and while it’s a simple model it sets a pattern that can be reused to apply more complex models down the road.


Background Flagging

This is Disorder’s main mode of operation, as it’s completely transparent to your users, that will not be aware of any changes.

Whenever a new post (or chat message using Discourse Chat) is created, it will be put in a classification queue asynchronously. In case that classification comes back above a configurable threshold, the post/chat message will be flagged so your moderation team is warned about it, and can make the final decision on the flag.

New post intervention

If you think that prevention is the best medicine, you may be interested in this more active option.

You can enable a synchronous classification of any new post, that if above a configurable threshold of toxicity, will trigger an intervention on the new post flow, asking the user to review and make amends to the message that may be outside of the boundaries set by your community rules.

This will only happen once, and after closing the modal the user will be able to post normally.

How does it work?

This plugin integrates the open source models from Detoxify, using a remote API call model to allow admins to properly scale the inference rate to each community needs.

We provide a simple image that provides an thin HTTP API that Discourse will call to perform content classification, which can be ran either in the same server where you run Discourse, or in a different server altogether.

The Discourse plugin listens to new post / new chat messages events, and enqueue a classification job in the background queue. Results are stored in the database so you can extract reports, and we flag content using a separate bot user so we can track it’s flag accuracy over time.


First, the plugin ships working out of the box, so it’s not necessary to change any setttings right away. However, if you want to change the plugin behavior, there are a few knobs you can use.

We provide 3 different classification models that you can pick on the plugin options:

  • unbiased (default): A model that tries to reduce the unintended model bias in toxicity classification

  • multilingual: A model that can classify Italian, French, Russian, Portuguese, Spanish and Turkish.

  • original: Most simple model.

You can also tweak if the plugin will:

  • automatically flag
  • enable sync intervention on toxic posts with warning (experimental)
  • enable sync intervention on toxic posts (not recommended)

All the above only happen when the comment is classified to be above the thresholds for each classification type:

  • toxicity
  • severe_toxicity
  • identity_attack
  • insult
  • threat
  • sexual_explicit

You can tweak each of the classification thresholds for automatic actions.

Classification Service

The plugin comes pre-configured working out of the box. For that, it’s contacting a service ran by Discourse (CDCK) to classify the user content. That classifier API service is open-source, and you can run your own copy of the service if necessary.


Just out of curiosity, what are the differences between “Disorder” and Discourse’s implementation of the Google Perspective API?


Code wise, they are completely different plugins.

From a ten foot view cover they same need, but they are engineered differently:

  • Disorder works with chat and posts, Perspective only with posts

  • Perspective relies on a proprietary and third party API, with all the privacy, reliability and transparence implications of that.

  • Disorder sets a pattern that allows the addition of new models easily, so we can evolve the service or even add brand new features

  • Disorder self-hostable API gives flexibility and freedom from pay-per API call and rate limits.

  • Disorder front-end surface is quite smaller, so it should be more resilient across Discourse updates.


Cool. Where and how do we do that?


Email team@discourse.org :slight_smile:


No need to reply here, but if you’re looking for suggestions about where to go next, an AI tag suggester based on a topic’s text could be useful. I’m imagining something similar to how Soundcloud suggest musical genre tags after they run an analysis on an upload. It’s useful for organizing user generated content on a busy site.


Do I understand correctly the disorder API instance should be launched to companion the plugin? There is a pre-filled setting disorder inference service api endpoint with https://disorder-testing.demo-by-discourse.com pre-set. Yet there is disorder inference service api key which is empty by default.

We are interested in giving this plugin a test as we face a lot of toxic behavior between users, which eventually gets resolved by flagging and help of Leaders, yet we would like to pro-actively prevent users from spreading negative posts if it’s possible, and this plugin seems to fit into such role.

Can we use any ready endpoint to give it a try? Fair warning, we have ~150k page views daily and it might clog up some unprepared servers.

We are standalone.


While you can run your own API server, the plugin comes pre-configured pointing to https://disorder-testing.demo-by-discourse.com/ so it works out of the box.

Please do use this endpoint for your instance, as it’s provided exactly for your use case of self-hosted instances wanting to give this plugin a try. In the default configuration, all the API calls happen in the background, so the API being down won’t impact your site in any way, so it’s safe to use it.

The api key setting is optional, and only needed if your API server have it enabled. The public instance at https://disorder-testing.demo-by-discourse.com/ doesn’t have it enabled.


Thank you! Sounds perfect and will give it a try in upcoming days :heart:


Are there other ML applications planned for the future?


I tried this for a week, and it was absurdly aggressive at flagging posts. I recommend using this only if you have a huge site without enough mods. Hope the AI gets better, but it’s just not there yet.


This is great feedback! Would you be willing to share some debugging stats to help me understand exactly what went down?

Something like the result of

  post_custom_fields AS pcf
  posts AS p ON p.id = pcf.post_id
  pcf.name = 'disorder'

here or in a PM would be immensely helpful.


Ahh yes, I forgot all about that! Here you go. There really weren’t that many, but they were just unnecessary and members and mods found them annoying. I also am unsure about it scanning DMs, I know there could be value there if someone is harassing someone via DM, but most of the time it’s just going to trigger people knowing that we’re looking at their DMs

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Do you use chat? Were all the annoying flags in posts / PMs ?

We do use chat but I’m pretty sure all the flags were in posts and pms

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First of all, I’m very grateful for both the feedback and the data you shared that allowed me to debug this further.

Now to my findings!

During this week, you had 1942 new posts from non-staff users. Quite an active community! However I would not say that the AI is " absurdly aggressive at flagging posts", as only 7 posts were flagged.

That said, of those 7, half are clearly false positives triggered by too low defaults thresholds, other half are trickier for AI to understand the context (calling your interlocutor a jerk vs telling a story about how someone was a jerk to you today while you were shopping) and one is, IMO, a correct hit.

If you are willing to give it another try, moving all the thresholds to 85 and moving to the original model may solve almost all trigger-happy flagging issues you had so far. I’ll add a site setting to allow skipping PMs as I can see how that can be annoying for some communities too.


Thanks Falco, I apologize for saying it was absurdly agressive. I had a lot of drama happening on the site already and the flagging just added to that and I was quite annoyed at the time.

I appreciate the suggestions and will give it another try. Question, what happens when you disable disorder flag automatically? Will I still be notified somehow if a post is deemed disorderly? This would be nice to test it out and figure out what settings work without having posts flagged.


Without that setting it will runs the posts against the AI but won’t take any actions. You can leave it like that and then run that Data Explorer query to do some analysis of the false positive/false negative rates.

There is also another setting that allows you to add groups to a skip list, where you could, for example, skip posts from TL4/3 from being classified. That may also help.

Dear @Falco,

We started testing Disorder out. The overall feedback is positive - it really does detect inappropriate things, while flagging a lot of things which our community accepts. Due to nature of the forum where we test this plugin (Adult), the communication involves several aspects which trigger Disorder to flag many many posts. Your SQL Query really does help checking out which thresholds to adjust, but may I suggest adding those to Reviewable Scoring table for each flagged post?

This one

I don’t know if it’s possible for a plugin to introduce it’s own data to this view, but it would help staff a lot to understand which criteria to adjust to reduce false-positive results for us. The way I see it is adding dropdown with a breakdown per criteria triggered within this view. No need to include criteria equaling 0. Those which are above 0, should be present there, but only those which exceed the current config thresholds should be marked bold/red.

Disorder Scoring example
  • Toxicity 65% [1]
  • Insult 73% [2]
  • Threat 12% [3]
  • Sexual explicit 2% [4]

If needed, I can provide you with SQL Query results. We are far from finishing reviewing Flag Queue…
We are using multilingual model and haven’t tried others. Decided it would be a good to start with considering we have some users who prefer posting using their original language.

  1. exceeding, red font ↩︎

  2. exceeding, red font ↩︎

  3. normal, normal font ↩︎

  4. normal, normal font ↩︎

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Hi again,

Wanted to let you know that we get Errors in logs related to Disorder using “original” model. I just switched it back to multilingual to see if it will make difference.

Job exception: undefined method `>=’ for nil:NilClass @classification[label] >= SiteSetting.send(“disorder_flag_threshold_#{label}”) ^^


/var/www/discourse/plugins/disorder/lib/classifier.rb:39:in `block in consider_flagging’

/var/www/discourse/plugins/disorder/lib/classifier.rb:38:in `filter’

/var/www/discourse/plugins/disorder/lib/classifier.rb:38:in `consider_flagging’

/var/www/discourse/plugins/disorder/lib/classifier.rb:25:in `classify!’

/var/www/discourse/plugins/disorder/app/jobs/regular/classify_post.rb:14:in `execute’

/var/www/discourse/app/jobs/base.rb:249:in `block (2 levels) in perform’

rails_multisite-4.0.1/lib/rails_multisite/connection_management.rb:80:in with_connection' /var/www/discourse/app/jobs/base.rb:236:in block in perform’

/var/www/discourse/app/jobs/base.rb:232:in `each’

/var/www/discourse/app/jobs/base.rb:232:in `perform’

sidekiq-6.5.8/lib/sidekiq/processor.rb:202:in `execute_job’

sidekiq-6.5.8/lib/sidekiq/processor.rb:170:in `block (2 levels) in process’

sidekiq-6.5.8/lib/sidekiq/middleware/chain.rb:177:in `block in invoke’

/var/www/discourse/lib/sidekiq/pausable.rb:134:in `call’

sidekiq-6.5.8/lib/sidekiq/middleware/chain.rb:179:in `block in invoke’

sidekiq-6.5.8/lib/sidekiq/middleware/chain.rb:182:in `invoke’

sidekiq-6.5.8/lib/sidekiq/processor.rb:169:in `block in process’

sidekiq-6.5.8/lib/sidekiq/processor.rb:136:in `block (6 levels) in dispatch’

sidekiq-6.5.8/lib/sidekiq/job_retry.rb:113:in `local’

sidekiq-6.5.8/lib/sidekiq/processor.rb:135:in `block (5 levels) in dispatch’

sidekiq-6.5.8/lib/sidekiq.rb:44:in `block in module:Sidekiq’

sidekiq-6.5.8/lib/sidekiq/processor.rb:131:in `block (4 levels) in dispatch’

sidekiq-6.5.8/lib/sidekiq/processor.rb:263:in `stats’

sidekiq-6.5.8/lib/sidekiq/processor.rb:126:in `block (3 levels) in dispatch’

sidekiq-6.5.8/lib/sidekiq/job_logger.rb:13:in `call’

sidekiq-6.5.8/lib/sidekiq/processor.rb:125:in `block (2 levels) in dispatch’

sidekiq-6.5.8/lib/sidekiq/job_retry.rb:80:in `global’

sidekiq-6.5.8/lib/sidekiq/processor.rb:124:in `block in dispatch’

sidekiq-6.5.8/lib/sidekiq/job_logger.rb:39:in `prepare’

sidekiq-6.5.8/lib/sidekiq/processor.rb:123:in `dispatch’

sidekiq-6.5.8/lib/sidekiq/processor.rb:168:in `process’

sidekiq-6.5.8/lib/sidekiq/processor.rb:78:in `process_one’

sidekiq-6.5.8/lib/sidekiq/processor.rb:68:in `run’

sidekiq-6.5.8/lib/sidekiq/component.rb:8:in `watchdog’

sidekiq-6.5.8/lib/sidekiq/component.rb:17:in `block in safe_thread’

Details 2
process_id 65460
application_version 2f8ad17aed81bbfa2fd20b6cc9210be92779bd74
current_db default
job Jobs::ClassifyPost
problem_db default
time 1:52 pm
post_id 604063
current_site_id default

P.S. Yes, multilingual mode does not produce these errors. Unbiased model does not produce errors either

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