Django community trained Bayesian inference based comment moderation app.

django-moderator integrates Django's comments framework with SpamBayes to classify comments into one of four categories, ham, spam, reported or unsure, based on training by users (see Paul Graham's A Plan for Spam for some background).

Users classify comments as reported using a report abuse mechanic. Staff users can then classify these reported comments as ham or spam, thereby training the algorithm to automatically classify similarly worded comments in future. Additionally comments the algorithm fails to clearly classify as either ham or spam will be classified as unsure, allowing staff users to manually classify them as well via admin.

Comments classified as spam will have their is_removed field set to True and as such will no longer be visible in comment listings.

Comments reported by users will have their is_removed field set to True and as such will no longer be visible in comment listings.

Comments classified as ham or unsure will remain unchanged and as such will be visible in comment listings.

django-moderator also implements a user friendly admin interface for efficiently moderating comments.

1. Install or add django-moderator to your Python path.
2. Add moderator to your INSTALLED_APPS setting.
3. Configure django-likes as described here.

4. Add a MODERATOR setting to your project's settings.py file. This setting specifies what classifier storage backend to use (see below) and also classification thresholds:

   MODERATOR = {
       'CLASSIFIER': 'moderator.storage.DjangoClassifier',
       'HAM_CUTOFF': 0.3,
       'SPAM_CUTOFF': 0.7,
       'ABUSE_CUTOFF': 3,
   }

   Specifically a HAM_CUTOFF value of 0.3 as in this example specifies that any comment scoring less than 0.3 during Bayesian inference will be classified as ham. A SPAM_CUTOFF value of 0.7 as in this example specifies that any comment scoring more than 0.7 during Bayesian inference will be classified as spam. Anything between 0.3 and 0.7 will be classified as unsure, awaiting further manual staff user classification. Additionally an ABUSE_CUTOFF value of 3 as in this example specifies that any comment receiving 3 or more abuse reports will be classified as reported, awaiting further manual staff user classification. HAM_CUTOFF, SPAM_CUTOFF and ABUSE_CUTOFF can be ommited in which case the default cutoffs are 0.3, 0.7 and 3 respectively.

5. Optionally, if you want an additional moderate object tool on admin change views, configure django-apptemplates as described here , include moderator as an INSTALLED_APP before django.contrib.admin and add moderator.admin.AdminModeratorMixin as a base class to those admin classes you want the tool available for.

1. By default all comments are classifed as they are created. You can however disable this behaviour by specifying REALTIME_CLASSIFICATION as False, i.e.:

   MODERATOR = {
       ...
       'REALTIME_CLASSIFICATION': False,
       ...
   }

2. By default moderator comment replies are posted chronologically after the comment being replied to. If however you need replies to be posted before the comment being replied to(for example if you display your comments reverse cronologically), you can specify REPLY_BEFORE_COMMENT as True, i.e.:

   MODERATOR = {
       ...
       'REPLY_BEFORE_COMMENT': True,
       ...
   }

django-moderator includes two SpamBayes storage backends, moderator.storage.DjangoClassifier and moderator.storage.RedisClassifier respectively.

To use moderator.storage.RedisClassifier as your classifier storage backend specify it in your MODERATOR setting, i.e.:

MODERATOR = {
    'CLASSIFIER': 'moderator.storage.RedisClassifier',
    'CLASSIFIER_CONFIG': {
        'host': 'localhost',
        'port': 6379,
        'db': 0,
        'password': None,
    },
    'HAM_CUTOFF': 0.3,
    'SPAM_CUTOFF': 0.7,
    'ABUSE_CUTOFF': 3,
}

You can also create your own backends, in which case take note that the content of CLASSIFIER_CONFIG will be passed as keyword agruments to your backend's __init__ method.

Once correctly configured you should use the traincommentclassifier management command to train the Bayesian inference system using a sample of existing comment objects (comments with is_removed as True will be trained as spam, ham otherwise), i.e.:

$ ./manage.py traincommentclassifier

Then you can periodically use the classifycomments management command to automatically classify comments as either ham, spam, reported or unsure based on user reports and previous training, i.e.:

$ ./manage.py classifycomments

Comments can be manually classified as either ham or spam via admin list view actions.