.env

EBOOKS_CONSUMER_KEY=TokenJUMBLE
EBOOKS_CONSUMER_SECRET=TokenJUMBLE
EBOOKS_OAUTH_TOKEN=TokenJUMBLE
EBOOKS_OAUTH_TOKEN_SECRET=TokenJUMBLE

bots SECURE.rb

#!/usr/bin/env ruby

require 'twitter_ebooks'
require 'dotenv'

include Ebooks

Dotenv.load('.env')

CONSUMER_KEY = ENV['EBOOKS_CONSUMER_KEY']
CONSUMER_SECRET = "TOKENjumble"
OAUTH_TOKEN = "TOKENjumble"
OAUTH_TOKEN_SECRET = "TOKENjumble"

ROBOT_ID = "ebooks" # Prefer not to talk to other robots
TWITTER_USERNAME = "crawley_ebooks" # Ebooks account username
TEXT_MODEL_NAME = "my_ebooks" # This should be the name of the text model

DELAY = 2..30 # Simulated human reply delay range, in seconds
BLACKLIST = ['tinysubversions', 'dril'] # users to avoid interaction with
SPECIAL_WORDS = ['JRPG', 'Hyperdimension Neptunia', 'Shin Megami Tensei', 'Shin Megami Tensei Persona', 'PSVita', 'PS3', 'PS4', 'Toronto', 'Proud Socialist', 'donate to Peter Coffin', 'donate to Kiva Bay', 'donate to Katherine Cross', 'donate to David Gallant', 'donate to Arielle Grimes', 'donate to Gamergate targets', 'cigarette', 'I miss my father', 'Dad was a good man', 'astrology', 'typical Aquarius', 'localize it Atlus', 'localize it NISA', 'typical Scorpio', 'my husband Tankboy', 'face it Gamergate', 'fuck Gamergate', 'infosec', 'information security', 'malware', 'globalization is bad', 'help the poor', 'half-Maltese', 'half-English', 'Canadian', 'Dad taught me to write', 'my husband knows secret stuff', 'dont fuck with me', 'my NES days', 'I love bots', 'how is Izzy bot', 'how is Markov bot', 'brave Brianna', 'brave Anita', 'how is InfoSec TSwift bot', 'my mistress is Kim Crawley', 'I read GamerGhazi', 'social justice is good', 'Kim Crawley is human me', 'give me wine gums', 'at the Eaton Centre', 'meet at Yonge Dundas Square', 'West Queen West is better', 'I live in the REAL Toronto', 'I support the NDP'] # Words we like
BANNED_WORDS = ['voldemort', 'evgeny morozov', 'heroku'] # Words we don't want to use

# Track who we've randomly interacted with globally
$have_talked = {}
$banned_words = BANNED_WORDS

# Overwrite the Model#valid_tweet? method to check for banned words
class Ebooks::Model
  def valid_tweet?(tokens, limit)
    tweet = NLP.reconstruct(tokens)
    found_banned = $banned_words.any? do |word|
      re = Regexp.new("\\b#{word}\\b", "i")
      re.match tweet
    end
    tweet.length <= limit && !NLP.unmatched_enclosers?(tweet) && !found_banned
  end
end

class GenBot
  def initialize(bot, modelname)
    @bot = bot
    @model = nil

    bot.consumer_key = CONSUMER_KEY
    bot.consumer_secret = CONSUMER_SECRET

    bot.on_startup do
      @model = Model.load("model/#{modelname}.model")
      @top100 = @model.keywords.top(100).map(&:to_s).map(&:downcase)
      @top50 = @model.keywords.top(20).map(&:to_s).map(&:downcase)
    end

    bot.on_message do |dm|
      bot.delay DELAY do
        bot.reply dm, @model.make_response(dm[:text])
      end
    end

    bot.on_follow do |user|
      bot.delay DELAY do
        bot.follow user[:screen_name]
      end
    end

    bot.on_mention do |tweet, meta|
      # Avoid infinite reply chains
      next if tweet[:user][:screen_name].include?(ROBOT_ID) && rand > 0.05

      author = tweet[:user][:screen_name]
      next if $have_talked.fetch(author, 0) >= 5
      $have_talked[author] = $have_talked.fetch(author, 0) + 1

      tokens = NLP.tokenize(tweet[:text])
      very_interesting = tokens.find_all { |t| @top50.include?(t.downcase) }.length > 2
      special = tokens.find { |t| SPECIAL_WORDS.include?(t) }

      if very_interesting || special
        favorite(tweet)
      end

      reply(tweet, meta)
    end

    bot.on_timeline do |tweet, meta|
      next if tweet[:retweeted_status] || tweet[:text].start_with?('RT')
      author = tweet[:user][:screen_name]
      next if BLACKLIST.include?(author)

      tokens = NLP.tokenize(tweet[:text])

      # We calculate unprompted interaction probability by how well a
      # tweet matches our keywords
      interesting = tokens.find { |t| @top100.include?(t.downcase) }
      very_interesting = tokens.find_all { |t| @top50.include?(t.downcase) }.length > 2
      special = tokens.find { |t| SPECIAL_WORDS.include?(t) }

      if special
        favorite(tweet)
        favd = true # Mark this tweet as favorited

        bot.delay DELAY do
          bot.follow author
        end
      end

      # Any given user will receive at most one random interaction per 12h
      # (barring special cases)
      next if $have_talked[author]
      $have_talked[author] = $have_talked.fetch(author, 0) + 1

      if very_interesting || special
        favorite(tweet) if (rand < 0.5 && !favd) # Don't fav the tweet if we did earlier
        retweet(tweet) if rand < 0.1
        reply(tweet, meta) if rand < 0.1
      elsif interesting
        favorite(tweet) if rand < 0.1
        reply(tweet, meta) if rand < 0.05
      end
    end

    # Reset list of mention recipients every 12 hrs:
    bot.scheduler.every '12h' do
      $have_talked = {}
    end

    # 80% chance to tweet every 2 hours
    bot.scheduler.every '2h' do
      if rand <= 0.8
        bot.tweet @model.make_statement
      end
    end
  end

  def reply(tweet, meta)
    resp = @model.make_response(meta[:mentionless], meta[:limit])
    @bot.delay DELAY do
      @bot.reply tweet, meta[:reply_prefix] + resp
    end
  end

  def favorite(tweet)
    @bot.log "Favoriting @#{tweet[:user][:screen_name]}: #{tweet[:text]}"
    @bot.delay DELAY do
      @bot.twitter.favorite(tweet[:id])
    end
  end

  def retweet(tweet)
    @bot.log "Retweeting @#{tweet[:user][:screen_name]}: #{tweet[:text]}"
    @bot.delay DELAY do
      @bot.twitter.retweet(tweet[:id])
    end
  end
end

def make_bot(bot, modelname)
  GenBot.new(bot, modelname)
end

Ebooks::Bot.new(TWITTER_USERNAME) do |bot|
  bot.oauth_token = OAUTH_TOKEN
  bot.oauth_token_secret = OAUTH_TOKEN_SECRET

  make_bot(bot, TEXT_MODEL_NAME)
end