2022 Moderation Report - Data Collection 5 (Users)

[sallytay]

Can we get stats from the site for the below; for the time period: 1 November 2021 - 31 October 2022
User Information

• Total number WDTK users

• Number of new users

• Number of banned users

• Number of user accounts anonymised at user's request in 2022

Data collection as last year's ticket #925

Deadlines are:
Data collection by 24 November
The report is scheduled to be compiled week beginning 27 November
The annual report is scheduled to go out on 15 December

[garethrees]

User accounts

require 'csv'

from_date = Time.zone.parse('2021-11-01').at_beginning_of_day
to_date = Time.zone.parse('2022-10-31').at_end_of_day
period = from_date..to_date

users_created_before_cutoff = User.where('created_at <= ?', to_date)
users_created_in_period = User.where(created_at: period)
users_updated_in_period = User.where(updated_at: period)

# Total number of users created before the end date who've confirmed their email
active_total = users_created_before_cutoff.where(email_confirmed: true).count

# Number of users created within the given period who are still active
# i.e. confirmed their email, have not been banned, and have not closed
# their account.
new_in_period = users_created_in_period.active.count

csv = CSV.generate do |csv|
  csv << ['User accounts', 'Total']

  csv << [
    'WhatDoTheyKnow users with activated accounts', active_total
  ]

  csv << [
    "New user accounts activated in #{to_date.year}", new_in_period
  ]
end

puts csv
# User accounts,Total
# WhatDoTheyKnow users with activated accounts,239540
# New user accounts activated in 2022,16217

User accounts	Total
WhatDoTheyKnow users with activated accounts	239540
New user accounts activated in 2022	16217

[garethrees]

Banned users

require 'csv'

from_date = Time.zone.parse('2021-11-01').at_beginning_of_day
to_date = Time.zone.parse('2022-10-31').at_end_of_day
period = from_date..to_date

users_updated_in_period = User.where(updated_at: period)

# Number of users where the last update was in the period who are marked
# as banned for spamming.
#
# Note that these are *identified* spammers; likely to be significantly
# more in reality
banned_spam =
  users_updated_in_period.where(ban_text: 'Banned for spamming').count

# Number of users where the last update was in the period who have been
# banned for some other reason than obvious spam. 
banned_other =
  users_updated_in_period.
    where.not(ban_text: '').
    where.not(ban_text: 'Banned for spamming').
    count

total_banned = banned_spam + banned_other

csv = CSV.generate do |csv|
  csv << ["Reason for banning users in #{to_date.year}", 'Total']

  csv << [
    'Spam', banned_spam
  ]

  csv << [
    'Other site misuse', banned_other
  ]

  csv << [
    "Total number of users banned in #{to_date.year}", total_banned
  ]
end

puts csv
# Reason for banning users in 2022,Total
# Spam,2160
# Other site misuse,300
# Total number of users banned in 2022,2460

Reason for banning users in 2022	Total
Spam	2160
Other site misuse	300
Total number of users banned in 2022	2460

[garethrees]

Anonymised users

require 'csv'

from_date = Time.zone.parse('2021-11-01').at_beginning_of_day
to_date = Time.zone.parse('2022-10-31').at_end_of_day
period = from_date..to_date

users_created_in_period = User.where(created_at: period)
users_updated_in_period = User.where(updated_at: period)

# Number of users created within the given period who have been
# anonymised
created_anon = users_created_in_period.where(name: '[Name Removed]').count

# Number of users where the last update was in the period who have been
# anonymised
updated_anon = users_updated_in_period.where(name: '[Name Removed]').count

total_anonymised = created_anon + updated_anon

csv = CSV.generate do |csv|
  csv << ['Anonymisation*', 'Total']

  csv << [
    "Accounts anonymised in #{to_date.year}", total_anonymised
  ]
end

puts csv
# Anonymisation*,Total
# Accounts anonymised in 2022,139

Anonymisation*	Total
Accounts anonymised in 2022	139