Wikimania 2009: Answers Community Moderation

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Yahoo! Answers is the largest Q&A service with hundreds of thousands of questions asked and answered every day. Traditional moderation systems failed to scale with the products growth. To address these challenges the team deployed a Community Moderation system which empowering trusted Answers members to report and remove abusive content automatically. One of the challenges to making the system work was to understand who to trust. The Answers team built a rich reputation model based on an analysis of over a dozen different system actions. Creating a successful community moderation system required changes in technology, community guidelines & policy, and user experience design.

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  • Wikimania 2009: Answers Community Moderation

    1. 1. Yahoo! Answers Community Moderation <ul><li>Micah Alpern, Director of User Experience Yahoo! Search </li></ul>
    2. 2. <ul><li>Yahoo! Answers Ask, Answer, Discover </li></ul><ul><li>1.2 million Q&A per day </li></ul><ul><li>Experiential Knowledge </li></ul>
    3. 3. <ul><li>Yahoo! Answers Ask, Answer, Discover </li></ul><ul><li>1.2 million Q&A per day </li></ul><ul><li>Experiential Knowledge </li></ul>
    4. 4. Activity
    5. 5. Activity
    6. 7. Answers is a marketplace <ul><li>People want lots of good answers to their questions quickly. </li></ul><ul><li>This means wide distribution of your freshest content. </li></ul><ul><li>Higher visibility for abuse in Questions than Answers. </li></ul>“ If you don't fix it right away, it's not worth fixing.” by getxsickphotography
    7. 8. Traditional Moderation Failed <ul><li>As Answers grew traditional moderation methods failed to scale with the community. </li></ul><ul><li>Manual customer care systems: </li></ul><ul><li>Had slow response times </li></ul><ul><li>Treated all abuse reports the same </li></ul><ul><li>Had high false positive and false negative rates </li></ul><ul><li>Were high cost and scaled up with traffic </li></ul>
    8. 9. Machine learning? A team of PHDs worked on a black box abuse classifier. <ul><li>Pre-filter of bad content to be reviewed by Customer Care. </li></ul><ul><li>Could only handle questions, not the answers </li></ul><ul><li>After a while that broke as well </li></ul><ul><li>60% of reports were fake abuse by users trying to report others. </li></ul>
    9. 10. Community MOderation <ul><li>Deployed a new Community Moderation system </li></ul><ul><li>  Empowered trusted Answers users to help moderate content by allowing their report abuse actions to automatically delete content. </li></ul><ul><li>  System didn’t reveal reputation scores to users, </li></ul><ul><li>Encouraged them to report accurately so they could gain more community influence. </li></ul>Illustration by Bryce Glass To address these challenges:
    10. 11. Reputation System
    11. 12. Answers Moderation Model Hide Content Item? Content Reporter Author
    12. 13. Answers Moderation Model REPORTER Abuse Suspicion Reputation REPORTER Community Investment Reputation User’s Abusive Reputation Confirmed Abuse Reporter Reputation Abuse Reporter Bootstrap Reputation Abuse Reporter Reputation CONTENT ITEM Abuse Reputation AUTHOR Overall Asker Reputation AUTHOR Overall Answerer Reputation AUTHOR Overall Asker Reputation Question Quality Reputation AUTHOR Overall Answerer Reputation Answer Quality Reputation Hide Content Item?
    13. 14. the story of a report an abuse report comes in We need to decided: Who do we believe? Do we hide the content? A question is asked.. Author Reporter
    14. 17. The content A question is asked.. <ul><li>What do we know about the content? </li></ul><ul><li>Have others reported it </li></ul><ul><li>Machined learned “Junk detector” score </li></ul><ul><ul><li>Language specific </li></ul></ul><ul><ul><li>Requires tons of training data </li></ul></ul><ul><ul><li>!= a quality detector </li></ul></ul>CONTENT ITEM Abuse Reputation
    15. 18. the reporter What do we know about the reporter? Reporter CONTENT ITEM Abuse Reputation <ul><li>Past reporting performance </li></ul><ul><li>and it’s outcome </li></ul>08.17.08 4:41pm 08.20.08 6:23 pm 10.04.08 1:08 pm 11.06.08 7:03 am Past reporting behavior hidden not hidden hide overturned Outcome not hidden Confirmed Abuse Reporter Reputation Abuse Reporter Reputation For many reporters we don’t have enough feedback.
    16. 19. How do we solve the cold start problem?
    17. 20. Cold Start: Reporter Reputation Reporter <ul><li>Suspicion: Implied Karma </li></ul><ul><li>Hard evidence </li></ul><ul><li>Examine signals across synonymous identities </li></ul>Confirmed Abuse Reporter Reputation Abuse Reporter Reputation CONTENT ITEM Abuse Reputation Take this result and update the running reputation of the content reported. Reporter’s Abuse Suspicion Reputation Reporter’s Community Investment Reputation User’s Abusive Reputation Abuse Reporter Bootstrap Reputation
    18. 21. Compared to Illustration by Bryce Glass
    19. 22. Do we trust the author? How good are they at Asking questions? How good are they at Answering? Do they have a history of abuse? AUTHOR Overall Asker Reputation AUTHOR Overall Answerer Reputation AUTHOR Overall Asker Reputation Question Quality Reputation AUTHOR Overall Answerer Reputation Answer Quality Reputation Users Abusive Reputation
    20. 23. we make a decision an abuse report comes in A question is asked.. Reporter Author
    21. 26. decisions can be revised an abuse report comes in A question is asked.. Reporter Trust level is re-instated and temporary protection assigned. Trust level for Reporting is decreased. Author
    22. 27. Naive moderation <ul><li>CraigsList </li></ul><ul><li>After 3 flags the post is deleted </li></ul><ul><li>Other systems: Ignore new user flags </li></ul><ul><li>In Answers: </li></ul><ul><li>Its harder to remove content from good contributors. </li></ul><ul><li>Easier for a even a new person to remove a spammer. </li></ul>
    23. 28. So how did we do?
    24. 29. Speed
    25. 30. Accuracy
    26. 31. Concussion <ul><li>Reputation system helped Answers: </li></ul><ul><li>Scale & empower the community to mange abuse </li></ul><ul><li>Provide a better experience to Askers and Answerers </li></ul>
    27. 32. Questions? Bryce Glass Randy Farmer Ori Zaltzman http://buildingreputation.com / Micah Alpern [email_address] twitter:malpern
    28. 34. SPAM <ul><li> “ We have other methods for identifying and dealing with spam, but as a very popular site we are a target for spammers so are continually upping our game to find new ways to identify them quicker and remove their content faster (though we shouldn’t share the methods we take). We recognize spam damages the experience for our users and so are committed to reducing spam on Answers as much as possible.” </li></ul>
    29. 35. Chat and Low quality content <ul><li>“ You mention this a bit in the presentation, but generally we acknowledge there are different types of questions being asked and answered on the site and accept that currently we’re limited in how we are identifying and highlighting knowledge based Q&A vs Social Q&A. However, we are looking at ways to easier promote the type of content a user wants to see.” </li></ul>
    30. 36. Accuracy <ul><li>“ focus is on experiential knowledge, practical help based on someone’s opinion or experience. Different type of knowledge than covered by wikipedia. We don’t want to publish content which is misleading, incorrect or harmful but use the “report abuse” system and our trusted users to balance any dubious advice. </li></ul>

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