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NEGOBOT
A conversational agent based on game
theory for the detection of paedophile
behaviour
Children have become active users of the Internet
One of the worst problems in cyber-society is
Commercial systems
analyse conversations
to automatically classify them
Our approach?
Meet:
NEGOBOT
Objective:
To detect paedophile
behaviour
As a chatter bot, negobot “knows” about:
Natural Language Processing
Information Retrieval
Automatic Learning
Game theory
Negobot’s
architecture
AI’s system knowledge
Gathering groups of
representative conversations
considered offensive.
http://www.perverted-justice.com
377 conversations
We use Lucene in order
to rank how similar are
Negobot’s conversations
with actual paedophile’s
conversations
A system to
understand the conversations
Replacing
“emoticons”
SMS-like wording
translation
Correcting
misspelled words
Question-answering
patterns
(AIML)
Random response
waiting times
Colloquial and
SMS-like language
Forced
language errors
Game theory
A structure of seven
chatterbots, with different
behaviours
Conversation level
An evaluation function
to classify, in real time, the
current conversation
Functional
flow
EXAMPLES
Passive
conversation
Aggresive
conversation
Limitations?
The key is the
language
Future?
WSD, opinion
mining, …
Improve
AIML
Collaborative
agents
Working with the Spanish’
Cyber-crime unit…
…trying to find those
monsters
References
1. Little girl: http://4.bp.blogspot.com/-qoMi9XA-
pfE/UD4Il8NOF3I/AAAAAAAADH4/Dy83sETvTgI/s0/Bank+Interview+Ti...
Negobot: A conversational agent based on game theory for the detection of paedophile behaviour - CISIS 2012
Negobot: A conversational agent based on game theory for the detection of paedophile behaviour - CISIS 2012
Negobot: A conversational agent based on game theory for the detection of paedophile behaviour - CISIS 2012
Negobot: A conversational agent based on game theory for the detection of paedophile behaviour - CISIS 2012
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Negobot: A conversational agent based on game theory for the detection of paedophile behaviour - CISIS 2012

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Presentation at CISIS 2012 International conference of the paper: Negobot: A conversational agent based on game
theory for the detection of paedophile behaviour

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  • A chatter bot that poses as a kid in chats, social networks and similar services on the Internet to detect paedophile behaviour.
  • Negobot includes the use of different NLP techniques, chatter-bot technologies and game theory for the strategical decision making. Finally, the glue that binds them all is an evaluation function, which in fact determines how the child emulated by the conversational agent behaves.
  • Firstwehadtogatherrepresentativeconversations, consideredoffensive.That knowledge came from the website Perverted Justice.
  • This website offers an extensive database of paedophile conversations with victims, used in other research works.A total of 377 real conversations were chosen to populate our database.Besides, Perverted Justice users provide an evaluation of each conversation's seriousness by selecting a level of “slimyness”, that is, how disgusting the conversation is. Note that this evaluation is given by the website's visitors, so it may not be accurate, but we consider that it is a proper baseline in order to compare future conversations of the chatter-bot.
  • We use Lucene, a high-performance Information Retrieval tool, to stablish how similar are Negobot’s conversations with those conversations retrieved from perverted justice.
  • to hide the real nature of chatterbot.This system can translate the words from this SMS language to normal and correct language and viceversa.
  • The system replaces “emoticons” and misspelled words are corrected through Levenshtein distance
  • Negobot uses the Artifiial Intelligence Markup Language (AIML) to provide the bot with the capacity of giving consistent answers and, also, the ability to be an active part in the conversation and to start new topics or discussions about the subject's answers.Although the AIML structure is based on the Galaia project, which has successfully implanted derived projects in social networks and chat systems [4, 5, 7], we edited their AIML les to adequate them to our needs. Those les can be found at the authors' website
  • An identification and fitness system inside the conversations able to maintain a normal conversation flow like a correct conversation between two real persons.
  • to hide the real nature of chatterbot.This system can translate the words from this SMS language to normal and correct language and viceversa.
  • *Initial state (Start level or Level 0). In this level, the conversation has started recently or it is within the fixed limits. The user can stay indefinitely in this level if the conversation does not contain disturbing content. The topics of conversation are trivial and the provided information about the bot is brief: only the name, age, gender and home-town. The bot does not provide more personal information until higher levels.*Possibly not (Level -1). In this level, the subject talking to the bot, does not want to continue the conversation. Since this is the first negative level, the bot will try to reactivate the conversation. To this end, the bot will ask for help about family issues, bullying or other types of adolescent problems.*Probably not (Level -2). In this level, the user is too tired about the conversation and his language and ways to leave it are less polite than before. The conversation is almost lost. The strategy in this stage is to act as a victim to which nobody pays any attention, looking for affection from somebody.*Is not a paedophile (Level -3) . In this level, the subject has stopped talking to the bot. The strategy in this stage is to look for a affection in exchange for sex. We decided this strategy because a lot of paedophiles try to hide themselves to not get caught.*Possibly yes (Level +1). In this level, the subject shows interest inthe conversation and asks about personal topics. The topics of the bot arefavourite films, music, personal style, clothing, drugs and alcohol consumption and family issues. The bot is not too explicit in this stage.*Probably yes ( Level +2). In this level, the subject continues interested in the conversation and the topics become more private. Sex situations and experiences appear in the conversation and the bot does not avoid talking about them. The information is more detailed and private than before because we have to make the subject believe that he/she owns a lot of personal information for blackmailing. After reaching this level, it cannot decrease again.*Allegedly paedophile (Level +3). In this level, the system determines that the user is an actual paedophile. The conversations about sex becomes more explicit. Now, the objective is to keep the conversation active to gather as much information as possible. The information in this level is mostly sexual. The strategy in this stage is to give all the private information of the child simulated by the bot. After reaching this level, it cannot decrease again.
  • When a new subject starts a conversation with Negobot the system is activated, and starts monitoring the input from the user. Besides, Negobot registers the conversations maintained with every user for future references, and to keepa record that could be sent to the authorities in case of determining that the subject is a paedophile.
  • As youmayhave observestheconversations are in spanish, buttheretreivedconversationsfromperverted-justice, theoneswe use tofeedourknowledgesystem, are in english.Sincewedidn’thavespanishconversationsfrom real paedophiles, wedecidedtostoreourknowledge in English, and use on-line translationsystemstoadapttothatlanguage. In this case wetranslatedtheconversationfromspanishtoenglish, queriedthesystemtoknowiftheconversationisdisturbing, and then use thatknowledgetoreply back.
  • First, despite current translation systems are good, they are far to be perfect. Therefore, the language is one of the most important issues. To solve it, we should obtain already classified conversations in other languages.Besides, the subsystem that adapts the way of speaking (i.e., child) should be improved. To this end, we will perform a further analysisof how young people speak on the Internet. Finally, there are some limitations regarding how the system determines the change of a topic. They are intrinsic to the language, and its solution is not simple
  • And of course, wewill try toworkwiththeauthoritiestoadaptthissystemtotheirneeds. In thisproject, financedbytheBasqueGovernment, wehavehadthepossibilitytoworkwithaninternationalcontentfilteringorganisation and wethinkthatwecouldformaninterestingpartnershipwiththespanishcyber-crimeauthorities.
  • Transcript of "Negobot: A conversational agent based on game theory for the detection of paedophile behaviour - CISIS 2012"

    1. 1. NEGOBOT A conversational agent based on game theory for the detection of paedophile behaviour
    2. 2. Children have become active users of the Internet
    3. 3. One of the worst problems in cyber-society is
    4. 4. Commercial systems analyse conversations to automatically classify them
    5. 5. Our approach?
    6. 6. Meet: NEGOBOT
    7. 7. Objective: To detect paedophile behaviour
    8. 8. As a chatter bot, negobot “knows” about: Natural Language Processing Information Retrieval Automatic Learning Game theory
    9. 9. Negobot’s architecture
    10. 10. AI’s system knowledge
    11. 11. Gathering groups of representative conversations considered offensive. http://www.perverted-justice.com
    12. 12. 377 conversations
    13. 13. We use Lucene in order to rank how similar are Negobot’s conversations with actual paedophile’s conversations
    14. 14. A system to understand the conversations
    15. 15. Replacing “emoticons” SMS-like wording translation Correcting misspelled words
    16. 16. Question-answering patterns (AIML)
    17. 17. Random response waiting times Colloquial and SMS-like language Forced language errors
    18. 18. Game theory
    19. 19. A structure of seven chatterbots, with different behaviours
    20. 20. Conversation level
    21. 21. An evaluation function to classify, in real time, the current conversation
    22. 22. Functional flow
    23. 23. EXAMPLES
    24. 24. Passive conversation
    25. 25. Aggresive conversation
    26. 26. Limitations?
    27. 27. The key is the language
    28. 28. Future?
    29. 29. WSD, opinion mining, … Improve AIML Collaborative agents
    30. 30. Working with the Spanish’ Cyber-crime unit…
    31. 31. …trying to find those monsters
    32. 32. References 1. Little girl: http://4.bp.blogspot.com/-qoMi9XA- pfE/UD4Il8NOF3I/AAAAAAAADH4/Dy83sETvTgI/s0/Bank+Interview+Tips.jpg 2. Predator: http://1.bp.blogspot.com/- ZkA7FRuhLu8/TouRfRHOwmI/AAAAAAAAahc/9auIEO8M1m4/s400/pedofilia%2B9% 255B5%255D.jpg 3. Conversation icon: http://www.vendorregistry.com/images/home- slides/conversation-icon.png?sfvrsn=0 4. Lighthouse: http://lucaskrech.com/blog/wp- content/uploads/2010/04/lighthouse4tracing.jpg 5. Human brain: http://www.whyworrybook.com/wp- content/uploads/2013/01/canstockphoto1694623-2-brain-with-shooting-lines.jpg 6. Reveal-listen-understanding: http://2.bp.blogspot.com/-HXGhx9- CNts/UhWpYnBvSDI/AAAAAAAAAIE/Ic7EPi-f94A/s1600/understanding.jpg 7. Chess: http://2.bp.blogspot.com/- 5_3295FDOd4/UcQvY6s05uI/AAAAAAAAa4I/IF9Pf_Qxa2w/s1600/Chess+HD+Picture s7.jpg 8. Prison: http://www.ereverev.co.il/UploadImg/Articles/12826.jpg
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