85:'s motto "the encyclopedia anyone can edit", there exists a corollary, "the encyclopedia anyone can vandalize". While this is true, Knowledge (XXG) continues to succeed because, overall, the overwhelming majority of the community wants to benefit it, not vandalize it, and thus the constructive edits significantly outweigh those that are not constructive. Over time, this has led to the creation of many powerful tools to assist in monitoring
210:, it will classify changes as either having a vandal-like nature or not and broadcast that likelihood to the collaboration group. If vandalism is likely, clients can adapt and show this to the user more quickly, ensuring that the issue is addressed as quickly as possible. Clients may also use this information in determining how many collaborators should inspect the change.
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bots' presence; the bots are merely a supplement, not a requirement, for the system. This ensures that, should the bots fail for any reason, the system will still operate as normally. There is no single point of failure in the collaboration cloud, so a failure of any component in the cloud should not impact the other components.
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To accommodate learning, every time a user reviews a change, the user's decision over whether or not to revert is recorded. These results are fed back to the collaboration bot; declarations of vandalism vs. not vandalism by the collaboration users are compared with the bot's decisions and the AI is
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If these three issues can be addressed, the efficiency and quality of work done by recent changes and new pages patrollers will significantly improve, and, accordingly, so will the quality of the
Knowledge (XXG) community as a whole. CollabRC attempts to address these issues using a combination of
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bots which work with the users of the system to help prioritize changes and issue alerts regarding high-risk changes. The bots learn from the decisions of the users of CollabRC and then use that knowledge to make future decisions. Regardless, the system still distributes load properly without the
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Clients that have decided they will patrol the change will add it to a queue for the user. After it has been patrolled, the results are announced back to the collaboration group. If a response is not received in time, the collaboration group assumes that the patroller has been lost and will
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feed and supplementary information provided by suppliers to build a list of changes that need to be reviewed. Clients resolve between each other which client(s) should review the edit and then, after view, report back to suppliers with whether or not the edit was determined to be
97:, however one stone has been left relatively unturned: these tools do not try to distribute the load across all patrollers, resulting in the potential to "step on each other's toes" while monitoring pages. This tends to result in three major problems:
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to obtain an efficient feed of all changes without adversely affecting the web server. When a new change is made, the active clients in the collaboration group decide amongst themselves which client(s) will patrol the change, based on:
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re-evaluate who should patrol the change. This ensures that the change is not lost. Furthermore, if a patroller has run out of changes to patrol, the system will ensure that the workload given to that user is increased.
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As both community quality standards and vandal patterns change, the system must be able to adapt its understanding of high-risk threats. Accordingly, one of the collaborators in the collaboration group can be a
144:: clients and suppliers. Suppliers are primarily bots. They focus on monitoring recent changes and flagging potentially malicious edits for client review. Clients are primarily human, though
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for a false "confident positive" is extremely high, so this should only occur in cases where the bot is very certain that the change contains vandal-like behavior.
93:, and other activity on Knowledge (XXG). These tools have significantly benefited the community, especially in the speed at which pages can be patrolled and
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Changes left unpatrolled that result from improperly distributed work (a direct impact from many patrollers monitoring the same change at the same time)
61:, as well as others, and incorporates its own technologies for collaboration in an effort to optimize, distribute, and automate efforts.
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CollabRC addresses the problems of the existing tools by promoting active, automated collaboration between patrollers. Known as the
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When the bot becomes extremely confident about a particular vandalism act, it will revert it on its own, similar in manner to how
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technology operates) to connect a group of users collaboratively. In addition, CollabRC connects each user to the
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The initial plan is being actively developed during the feedback phase. The plan is drafted and maintained using
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A loss of potential knowledge that occurs from not recognizing vandalism patterns that occur on
Knowledge (XXG)
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CollabRC is currently in its "motivation feedback" phase. Input on the following topics is strongly desired:
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can be contacted to pull changes from other developers which will then be merged in after
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list to classify edits. The first part of the bot can be taught. Only users not in the
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is intended to revert edits that the other bots are confident are vandalism.
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adapted to improve accuracy. This is similar in mechanic to how adaptive
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CollabRC connects to either a well-known communications server (such as
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This bot blacklists, and flags edits by users that are reverted by
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of participants (some human, some bots) comprised of two primary
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is a bot that acts as a client. They read information from the
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