Related task with lots of information: T241921: Fix Wikimedia captchas

@leila , If this task is still opened I would like to help.

In addition to captchas, I see 2 possible ways how to handle wiki spambots depending on the scale of the issue.

Small scale

• if the issue is specific to small wikis and creation of one-off pages, then simpler/statistical methods should be enough:
  • check for excessive spelling or grammar mistakes
  • special and other characters count as metric with thresholds (deltas library could be utilized for this https://github.com/halfak/deltas , related to https://phabricator.wikimedia.org/T111179)

Medium/Large scale

• if there are pages/series of pages generated by spambots or groups of spambots, then machine leraning approach could be used
  • use [IP address, username, requested webpage URL, session identity, timestamp] information from web server logs to identify wiki usage patterns by spambots
  • gather, create and preprocess features dataset specific to wikimedia and apply ML methods for SPAM detection:
  • example of featureset from "Emotional Bots Content-based Spammer Detection on Social Media":
    
  • add SPAM topic detection to topic detection models : https://github.com/wikimedia/drafttopic
  • for ML spambot detection methods reference see Reference section: https://github.com/IUNetSci/botometer-python

Note:
Please let me know what do you think. Do you have any examples of spam pages? Do you prefer simple or more robust solution?