Building Web Reputation Systems- P20

Building Web Reputation Systems- P20:Today’s Web is the product of over a billion hands and minds. Around the clock andaround the globe, people are pumping out contributions small and large: full-lengthfeatures on Vimeo, video shorts on YouTube, comments on Blogger, discussions onYahoo! Groups, and tagged-and-titled Del.icio.us bookmarks. User-generated contentand robust crowd participation have become the hallmarks of Web 2.0.
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Building Web Reputation Systems- P20 • Some community members would report abuse for altruistic reasons: out of a desire to keep the community clean. (See the section “Altruistic or sharing incen- tives” on page 113.) Downplaying the contributions of such users would be critical; the more public their deeds became, the less likely they would continue acting out of sheer altruism. • Some community members had egocentric motivations for reporting abuse. The team appealed to those motivations by giving those users an increasingly greater voice in the community.The High-Level Project ModelThe team devised this plan for the new model: a reputation model would sit betweenthe two existing systems—a report mechanism that permitted any user on Yahoo! An-swers to flag any other user’s contribution and the (human) customer care system thatacted on those reports. (See Figure 10-3.)This approach was based on two insights: 1. Customer care could be removed from the loop—in most cases—by shifting the content removal process into the application and giving it to the users, who were already the source of the abuse reports, and then optimizing it to cut the amount of time and offensive posting by 90%. 2. Customer care could then handle just the exceptions—undoing the removal of content mistakenly identified as abusive. At the time, such false positives made up 10% of all content removal. Even if the exception rate stayed the same, customer care costs would decrease by 90%.The team would accomplish item 1, removing customer care from the loop, by imple-menting a new way to remove content from the site—“hiding.” Hiding involved trust-ing the community members themselves to vote to hide the abusive content. Thereputation platform would manage the details of the voting mechanism and any relatedkarma. Because this design required no external authority to remove abusive contentfrom view, it was probably the fastest way to cut display time for abusive content.As for item 2, dealing with exceptions, the team devised an ingenious mechanism—anappeals process. In the new system, when the community voted to hide a user’s content,the system sent the author an email explaining why, with an invitation to appeal thedecision. Customer care would get involved only if the user appealed. The team pre-dicted that this process would limit abuse of the ability to hide content; it would providean opportunity to inform users about how to use the feature; and, because trolls oftendon’t give valid email addresses when registering an account, they would simply beunable to appeal because they’d never receive the notices. Initial Project Planning | 251Figure 10-3. The system would use reputation as a basis for hiding abusive content, leaving staff tohandle only appeals.Most of the rest of this chapter details the reputation model designated by the HideContent? diamond in Figure 10-3. See the patent application for more details about theother (nonreputation) portions of the diagram, such as the Notify Author and Appealsprocess boxes. Yahoo! has applied for a patent on this reputation model, and that ap- plication has been published: Trust Based Moderation—Inventors: Ori Zaltzman and Quy Dinh Le. Please consider the patent if you are even thinking about copying this design. We are grateful to both the Yahoo! Answers and the reputation product teams for sharing their design insights and their continued assistance in preparing this case study.Objects, Inputs, Scope, and MechanismYahoo! Answers was already a well-established service at the time that the communitycontent moderation model was being designed, with all of the objects and most of theavailable inputs already well defined. The final model includes dozens of inputs to morethan a dozen processes. Out of respect for intellectual property and the need for brevity,we have not detailed every object and input here. But, thanks to the Yahoo! Answersteam’s willingness to share, we’re able to provide an accurate overall picture of thereputation system and its application.The ObjectsHere are the objects of interest for designing a community-powered content moderationsystem:252 | Chapter 10: Case Study: Yahoo! Answers Community Content ModerationUser contributions User contributions are the objects that users make by either adding or evaluating content: Questions Arriving at a rate of almost 100 per minute, questions are the starting point of all Yahoo! Answers activity. New questions are displayed on the home page and on category pages. Answers Answers arrive 6 to 10 times faster than questions and make up the bulk of the reputable enti ...