Building Web Reputation Systems- P19

Building Web Reputation Systems- P19: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- P19Figure 9-1. By giving users a simple, private “Watchlist,” the Answers designers responded to theneeds of Abuse Reporters who wanted to check back in on bad content.See Chapter 10 for an in-depth case study on a more comprehensive project to not onlykeep bad content on Answers subdued, but actually clean it up and remove it altogether,with much greater accuracy and speed.Tuning for BehaviorThere are many useful sources for reputation input, but source stands out among allothers: the user. The vast majority of content on the Web is user-generated, and userfeedback generates the reputation that powers the Web. Even every search engine isbuilt on evaluations in the form of links provided not by algorithms, but by people.In an effort to optimize all of this people-powered value, reputation systems have cometo play a large part in creating incentives for user behavior: participation points, topcontributor awards, etc. Users then respond to these incentives, changing their behav-ior, which then requires the reputation systems to be tuned to optimize newer and moresophisticated behavior (including adjustments for undesirable side effects: aka abuse).The cycle then repeats, if you’re lucky.Emergent effects and emergent defectsIt’s quite possible that—even during the beta period of your deployment—you’re no-ticing some strange effects starting to take hold. Perhaps content items are rising in theranks that don’t entirely seem…deserving somehow. Or maybe you’re noticing a pre-dominance of a certain kind of content at the expense of other types. What you’re seeingis the character of your community shaking itself out, finding its edges, and definingitself. Tread carefully before deciding how (and if) to intervene.Check out Delicious’s Popular Bookmarks ranking for any given week; we bet you’llsee a whole lot of “Top N” blog articles (see Figure 9-2). Why might this be? Technologyessayist Paul Graham posits that it may be the users of the service, and their motiva-tional mindset, that explain it: “Delicious users are collectors, and a list of N thingsseems particularly collectible because it’s a collection itself.” (Graham explores the“List of N Things” phenomenon to some depth at http://www.paulgraham.com/nthings.html.) The preponderance of lists on Delicious is a natural offshoot of its context of236 | Chapter 9: Application Integration, Testing, and TuningFigure 9-2. What are people saving on Delicious? Lists, lists and more lists…(and there’s nothingwrong with that).use—an emergent effect—and is probably not one that you would worry about, nortry to control in any way.But you may also be seeing the effects of some design decisions that you’ve made, andyou may want to tweak those designs now before wider deployment. Blogger and socialmedia maven Muhammad Saleem noticed one such problem with voting on sociallydriven news sites such as Digg: We are beginning to see a trend where people make assumptions about the contents of an article based on the meta-data associated with the submission rather than reading the article itself. Based on these (oft-flawed) assumptions, people then vote for or against the stories, and even comment on the stories without having read the stories themselves. —http://web.archive.org/web/20061127130645/http://themulife.com/?p=256We’ve noticed a similar tendency on some community-voting sites we’ve worked onat Yahoo! and have come to consider behavior like this to be a type of emergent de-fect: behavior that is homegrown within the community and may even become a defacto standard for interacting, but is not necessarily valued. In fact, it’s basically abug and a failing of your system or—more likely—user interface design.In instances like these, you should consider tweaking your design, to encourage theproper and appropriate use of the controls you’re providing. In some ways, it’s not Tuning Your System | 237surprising that Digg users are voting on articles based on only surface appraisals; theapplication’s very design in fact encourages this (see Figure 9-3).Figure 9-3. The design of Digg enables (one might argue, encourages) voting for articles at a high levelof the site. This excerpted screen is the front page of Digg—users can vote for (Digg) an article, oragainst (bury) it, with no need to read further.Of course, one should not presuppose that the Digg folks think of this behavior (if it’seven as widespread as Saleem indicates) as a defect. Again, it’s a careful balance betweenthe actual observed behavior of users and your own predetermined goals and aspira-tions for the application.It’s quite possible that Digg feels that high voting levels—even if some percentage ofthose votes are from uninformed users—are important enough to promote voti ...