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Case Study: Yahoo! Answers Community Content Moderation [Building Web Reputation Systems]

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He refers to the unique focus of Yahoo! Answers, a lot of the conversations that take place there are intended to be social in nature. Micah has published a detailed presentation that covers this project in some depth. You can find it at http: Answers is not a reference site in the sense that Wikipedia is: Rather, its goal is to encourage participation from a wide variety of contributors.

That goal is important to keep in mind as we delve further into the problems that Yahoo! Answers was undergoing and the steps needed to solve them.

Specifically, keep the following in mind: In a marketplace for opinions such as Yahoo! Answers, it's in the best interest of everyone askers, answerers, and the site operator to encourage more opinions, not fewer. So the designer of a moderation system intended to weed out abusive content should make every attempt to avoid punishing legitimate questions and answers.

False positives can't be tolerated, and the system must include an appeals process. Attack of the Trolls So, what problems, exactly, was Yahoo! Two factors-the timeliness with which Yahoo! Answers displayed new content, and the overwhelming number of contributions it received-had combined to create an unfortunate environment that was almost irresistible to trolls.

Dealing with offensive and antagonistic user content had become the number one feature request from the Yahoo! It was intended to classify the worst of the worst content and put it into a prioritized queue for the attention of customer care agents. The Junk Detector was mostly a bust. It was moderately successful at detecting obvious spam, but it failed altogether to identify the subtler, more insidious contributions of trolls. Do Trolls Eat Spam? What's the difference between trolling behavior and plain old spam?

The distinction is subtle, but understanding it is critical when you're combating either one. We classify as spam communications that are unwanted, make overtly commercial appeals, and are broadcast to a large audience. Fortunately, the same characteristics that mark a communication as spam also make it stand out.

You probably can easily identify spam after just a quick inspection. We can teach these same tricks to machines. Although spammers constantly change their tactics to evade detection, spam generally can be detected by machine methods. Trollish behavior, however, is another matter altogether. Trolls may not have financial motives-more likely, they crave attention and are motivated by a desire to disrupt the larger conversation in a community.

Trolls quickly realize that the best way to accomplish these goals are by nonobvious means. An extremely effective means of trolling, in fact, is to disguise your trollish intentions as real conversation.

Accomplished trolls can be so subtle that even human agents are hard pressed to detect them. For these trolls, pretending to be faithful fans is part of the fun-it renders them all the more disruptive when they start to trash-talk the home team. How do you detect for that? It's hard for a human-and near impossible for a machine-but it's possible with a number of humans. Adding consensus and reputation-enabled methods makes it easier to reliably discern trollish behavior from sincere contributions.

Because a reputation system to some degree reflects the tastes of a community, it also has a better-than-average chance at catching behavior that transgresses those tastes. Engineering manager Ori Zaltzman recalls the exact moment he knew for certain that something had to be done about trolls-when he logged onto Yahoo! Answers to see the following question highlighted on the home page: That question got through the Junk Detector easily.

Even though it's an obviously unwelcome contribution, on the surface-and to a machine-it looked like a perfectly legitimate question: So abusive content could sit on the site with impunity for hours before the staff could respond to abuse reports. Time Was a Factor Because the currency of Yahoo! Answers functions best as a near-real-time communication system, and-as a design principle-erred on the side of timely delivery of users' questions and answers.

User contributions are not subject to any type of editorial approval before being pushed to the site. Early on, the Yahoo! Answers product plan did call for editor approval of all questions before publishing. This was an early attempt to influence the content quality level through by modeling good user behavior.

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The almost immediate, skyrocketing popularity of the site quickly rendered that part of the plan moot. There simply was no way that any team of Yahoo! Location, Location, Location One particular area of the site became a highly sought-after target for abusers: Any newly asked question could potentially appear in highly trafficked areas, including the following: Groups, Sports, or Music, where Yahoo!

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Answers content was syndicated Built with Reputation Yahoo! Answers, somewhat famously, already featured a reputation system-a very visible one, designed to encourage and reward ever-greater levels of user participation.

Answers, user activity is rewarded with a detailed point system. Answers point system is somewhat notorious in reputation system circles, and debate continues to rage over its effectiveness. At the heart of the debate is this question: Does it make the site a better source of information? Or are the system's game-like elements promoted too heavily, turning what could be a valuable, informative site into a game for the easily distracted?

We're mostly steering clear of that discussion here. This case study deals only with combating obviously abusive content, not with judging good content from bad.

Answers decided to solve the problem through community moderation based on a reputation system which would be completely separate from to the existing public participation point system. However, it would have been foolish to ignore the point system-it was a potentially rich source of inputs into any additional system.

The new system clearly would have to be influenced by the existence of the point system, but it would have to use the point system input in very specific ways, while the point system continued to function. The crew fielded to tackle this problem was a combination of two teams. Answers product team had ultimate responsibility for the application.

It was made up of domain experts on questions and answers: These were the folks who best understood the service, and they were held accountable for preserving the integrity of the user experience. Ori Zaltzman was the engineering manager, Quy Le was product manager, Anirudh Koul was the engineer leading the troll hunt and optimizing the model, and Micah Alpern was the lead user experience designer.

The members of the product team were the primary customers for the technology and advice of another team at Yahoo! Yvonne French was the product manager for the reputation platform; Building Web 2.

A small engineering team built the platform and implemented the reputation models. For example, it is unlikely that your organization will feature an engineering team specifically dedicated to architecting a reputation platform. However, you might consider drafting one or more members of your team to develop deep knowledge in that area. Here's how these combined teams tackled the problem of taming abuse on Yahoo!

What are your goals for your application? What is your content control pattern? Given your goals and the content models, what types of incentives are likely to work well for you? Setting Goals As is often the case on community-driven web sites, what is good for the community-good content and the freedom to have meaningful, interruption-free exchanges-also just happens to make for good business value for the site owners. This project was no different, but it's worth discussing the project's specific goals.

Cutting Costs The first motivation for cleaning up abuse on Yahoo! The existing system for dealing with abuse was expensive, relying as it did on heavy human-operator intervention.

Each and every report of abuse had to be verified by a human operator before action could be taken on it. Randy Farmer, at the time the community strategy analyst for Yahoo! Cleaning Up the Neighborhood The monetary cost of dealing with abuse on Yahoo! Answers was considerable, but the community cost of not dealing with it would have been far higher: Answers addressed the problem forcefully and with great vigor, the community would notice the effort and respond in kind.

The goals for content quality were twofold: Who Controls the Content? Let's revisit those patterns briefly for this project. Before the community content moderation project, Yahoo! Answers fit nicely in the basic social media pattern. While users were given responsibility for creating and editing voting for, or reporting as abusive questions and answers, final determination for removing content was left up to the staff.

The team's goal was to move Yahoo! That responsibility would be mediated by the reputation system, but staff intervention in content quality issues would only be necessary in cases where content contributors appealed the systems' decisions. Answers, the team decided to devise incentives that took into account a couple of primary motivations: Downplaying the contributions of such users would be critical: 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. Answers to flag any other user's contribution, and the human customer care system that acted on those reports. This approach was based on two insights: Customer care could then handle just the exceptions-undoing the removal of content mistakenly identified as abusive. The reputation platform would manage the details of the voting mechanism and any related karma.

Because this design required no external authority to remove abusive content from view, it was probably the fastest possible way to cut display time for abusive content. As for item 2, dealing with exceptions, the team devised an ingenious mechanism-an appeals 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 the decision.

Customer care would get involved only if the user appealed. The team predicted that this process would limit abuse of the ability to hide content; it would provide an opportunity to inform users about how to use the feature; and, because trolls often don't give valid email addresses when registering an account, they would simply be unable to appeal-they'd never receive the notices.

The system would use reputation as a basis for hiding abusive content, leaving staff to handle only appeals. Most of the rest of this chapter details the reputation model designated by just the Hide Content? See the patent application for more details about the other nonreputation portions of the diagram, such as the Notify Author and Appeals process boxes.

Trust Based Moderation -Inventors: Ori Zaltzman and Quy Dinh Le. Please consider the patent if you are even thinking about copying this design. The authors 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 Mechanism Yahoo!

Answers was already a well-established service at the time that the community content moderation model was being designed, with all of the objects and most of the available inputs already well defined. The final model includes dozens of inputs to more than a dozen processes. Out of respect for intellectual property and the need for brevity, we have not detailed every object and input here.

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But, thanks to the Yahoo! Answers team's willingness to share, we're able to provide an accurate overall picture of the reputation system and its application. Here are the objects of interest for designing a community-powered content moderation system. User Contributions User contributions are the objects that users make by either adding or evaluating content.

Questions Arriving at a rate of almost per minute, questions are the starting point of all Yahoo! 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 entities in the application.

All answers are associated with a single question and are displayed in chronological order, oldest first. Ratings After a user makes several contributions, the application encourages the user to rate answers with a simple thumb-up or thumb-down vote. The author of the question is also allowed to select the best answer and give it a rating on a 5-star scale. If the question author does not select a best answer in the allotted time, the community vote is used to determine the best answer. Users may also mark a question with a star, indicating that the question is a favorite.

Each of these rating schemes already existed at the time the community content moderation system was designed, so for each scheme, the inputs and the outputs were both available for the designers' consideration. Users All users in this application have two data records that can both hold and supply information for reputation calculations: Answers, which stores only application-specific fields. Developing this model required considering at least two different classifications of users: Authors Authors create the items questions and answers that the community can moderate.

Reporters Reporters determine that an item a question or an answer breaks the rules and should be removed. Customer Care Staff The customer care staff are the target of the model.

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The goal is to reduce the staff's participation in the content moderation process as much as possible, but not to zero. Any community content moderation process can be abused: Customer care would still evaluate appeals in those cases, but the number of such cases would be far fewer than the total number of abuses.

Customer care agents also have a reputation-for accuracy-though it isn't calculated by this model. At the start of the Yahoo! That rate meant that 1 in 10 submissions was either incorrectly deleted or incorrectly allowed to remain on the site. An important measure of the model's effectiveness was whether users' evaluations were more accurate than the staff's.

The design included two documents that are worthy of note, though they were not formal objects that is, they neither provided input nor were reputable entities.

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Users are supposed to apply these rules in evaluating content. Limiting Scope When a reputation model is introduced, users often are confused at first about what the reputation score means.

The design of the community content moderation model for Yahoo! Answers is only intended to identify abusive content, not abusive users. Remember that many reasons exist for removing content, and some content items are removed as a result of behaviors that authors are willing to change, if gently instructed to do so. The inclusion of an appeals process in the application not only provides a way to catch false-positive classification by reporters; it also gives Yahoo!

Answers - allowing for the user to learn more about expected behavior. An Evolving Model Ideally, in designing a reputation system, you'd start with as comprehensive a list of potential inputs as possible. In practice, when the Yahoo! Answers team was designing the community content moderation model, they used a more incremental approach.

As the model evolved, the designers added more subtle objects and inputs. Below, to illustrate an actual model development process, we'll roughly follow the historical path of the Yahoo! Abuse Reporting When you develop a reputation model, it's good practice to start simple: Assume a universe in which the model works exactly as intended. Don't focus too much on performance or abuse at first-you'll get to those issues in later iterations.

Trying to solve this kind of complex equation in all dimensions simultaneously will just lead to confusion and impede your progress. Answers community content moderation system, the designers started with a very basic model-abuse reports would accumulate against a content item, and when some threshold was reached, the item would be hidden. Craigslist is a well-known example.

Despite the apparent complexity of the final application, the model's simple core design remained unchanged: Having that core design to keep in mind as the key goal in mind helped eliminate complications in the design.

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Inputs From the beginning, the team planned for the primary input to the model to be a user-generated abuse report explicitly about a content item a question or an answer.

This user interface device was the same one already in place for alerting customer care to abuse. Though many other inputs were possible, initially the team considered a model with abuse reports as the only input. Abuse Report User Input Users could report content that violated the community guidelines or the terms of service. A Twitter user commented by urging the EFCC to investigate the proliferation of luxury boutiques, high rises and entertainment brands which often act as fronts for illicit activities.

Some of the so-called richest and most popular faces in the entertainment industry, for example, are alleged accomplices or accessories to fraud. We are caught in a terrible catch However, in a society where basic needs are not met education, health, clean water or foodnot only is being poor a death sentence, it is a trap one must escape at all costs. Nigerians are trapped in a dog-eat-dog society and impunity is the name of the game: Until we develop institutions with swift punishments regardless of who is on trial, until we reward hard work and long term thinking our popular culture will continue to tell our youth to do what they must to survive because it is what works.

After all, many of the examples of success in our society are people who made their money through dubious means. Many of these people at the top of the social pyramid are aided by the media in white-washing their antecedents. Evans EVANS THE alleged kidnapper whose story enthralled the nation reportedly often cries during his court appearances, further proof that we have little understanding of actions and their consequences: The greatest looters, killers, kidnappers, etc.

Let me face my trial alive; why do you people want to kill me? The Senate only seems to come alive when its members are in jeopardy no matter the evidence against them but no serious prison reform legislation to benefit the majority of Nigerians is ever advertised. But he seemingly focused on the rights of politicians rights to what, you might wonder; to evade questioning?