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this probability. The higher the threshold, the fewer false positives, but also less vandalism is caught. A threshold is selected by assuming a fixed false positive rate (percentage of constructive edits incorrectly classified as vandalism) and optimizing the amount of vandalism caught based on that. This means that there will always be some false positives, and it will always be at around the same percentage of constructive edits. The current setting of the false positive rate is listed in
Statistics above.
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vandal-fighters. This list of edits is called a corpus or dataset. The accuracy of the bot largely depends on the size and quality of the dataset. If the dataset is small, contains inaccurately classified edits, or does not contain a random sampling of edits, the bot's performance is severely hampered. The best thing you and other
Wikipedians can do to help the bot is to improve the dataset. If you're interested in helping out, please see the Dataset Review Interface section.
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935:. Please ask the devs for access. If you would like to run the bot for yourself on your own wiki, you should discuss with the devs all the factors involved in making it work properly. You should also be aware that it will only run on a Linux/UNIX system, and the source code can be rather difficult to compile (many dependencies) unless you're experienced with Linux/UNIX systems.
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1534:(wow, that’s almost as much as the English Knowledge’s article count!) and counting to fight lots of vandalism. ClueBot III gets the other half Barnstar for doing a lot of talk page archiving. Thank you so much for all your hard work, and here’s to another 5 million edits of reverting vandalism, and another 600,000+ of talk page archiving!
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it will not prevent false positives, it may help to reduce the number of good-quality edits that are false positives. Also, if the bot's accuracy improves so much that the false positive rate can be reduced without a significant drop in vandalism catch rate, we may be able to reduce the overall number of false positives.
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false positives, but they do happen. If one of your edits is incorrectly identified as vandalism, simply redo your edit, remove the warning from your talk page, and if you wish, report the false positive. ClueBot NG is not (yet) sentient — it is an automated robot, and if it incorrectly reverts your
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For the bot to be effective, the dataset needs to be expanded. Our current dataset has some degree of bias, as well as some inaccuracies. We need volunteers to help review edits and classify them as either vandalism or constructive. We hope to eventually completely replace our current dataset with
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We found that the Python dataset downloader we used to generate the training dataset does not generate data that is identical to the live downloader. It's possible that this is greatly reducing the effectiveness of the live bot. We're working on writing shared code for live downloading and dataset
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After the core makes its primary vandalism determination, the data is given to the
Knowledge interface. The Knowledge interface contains some simple logic designed to reduce false positives. Although it also reduces vandalism catch rate a small amount, it also reduces false positive rate, and some
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When false positives occur, they may not be poor quality edits, and there may not even be an apparent reason. If you report the false positive, the bot maintainers will examine it, try to determine why the error occurred, and if possible, improve the bot's accuracy for future similar edits. While
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called an
Artificial Neural Network that generates a probability that a given edit is vandalism. The probability is usually pretty close, but can sometimes be significantly different from what it should be. Whether or not an edit is classified as vandalism is determined by applying a threshold to
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To make sure the threshold and statistics are accurate and do not give inaccurate statistics or a higher false positive rate than expected, the portion of the dataset used for threshold calculations is kept separate from the training set, and is not used for training. Also, only the most accurate
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A few different
Bayesian classifiers are used in ClueBot NG. The most basic one works in units of words. Essentially, for each word, the number of constructive edits that add the word, and the number of vandalism edits that add the word, are counted. This is used to form a vandalism-probability
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than all previous anti-vandal bots, including the original ClueBot. Previous anti-vandal bots have used a list of simple heuristics and blacklisted words to determine if an edit is vandalism. If a certain number of heuristics matched, the edit was classified as vandalism. This method results in
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The threshold is not randomly chosen by a human, but is instead calculated to match a given false positive rate. When doing actual vandalism detection, it's important to minimize false positives to a very low level. A human selects a false positive rate, which is the percentage of constructive
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The ANN generates a vandalism score between 0 and 1, where 1 is 100% sure vandalism. To classify some edits as vandalism, and some as constructive, a threshold must be applied to the score. Scores above the threshold are classified as vandalism, and scores below the threshold are classified as
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is a machine learning technique that can recognize patterns in a set of input data that are more complex than simply determining weights. The input to the ANN used in ClueBot NG is composed of a number of different statistics calculated from the edit, which include, among many other things, the
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Instead of a predefined list of rules that a human generates, ClueBot NG learns what is considered vandalism automatically by examining a large list of edits which are preclassified as either constructive or vandalism. Its concept of what is considered vandalism is learned from human
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Note that edits in the feed may not necessarily be in precise order, because ClueBot NG processes them in parallel. Non-reverted edits are usually processed in under a second. Reverted edits can sometimes take up to 10 seconds or more to process due to API lag on reverting.
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Scores from the
Bayesian classifiers alone are not used. Instead, they're fed into the neural network as simple inputs. This allows the neural network to reduce false positives due to simple blacklisted words, and to catch vandalism that adds unknown words.
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If you want to help significantly improve the bot's accuracy, you can make a difference by contributing to the review interface. This should help us more accurately determine a threshold, catch more vandalism, and eventually, reduce false positives.
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Note: These statistics are calculated before post-processing filters. Post-processing filters primarily reduce false positive rate (ie, the actual number of false positives will be less than stated here), but can also slightly reduce catch rate.
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As ClueBot NG requires a dataset to function, the dataset can also be used to give fairly accurate statistics on its accuracy and operation. Different parts of the dataset are used for training and trialing, so these statistics are not biased.
978:(unless your edit is vandalism). False positives are rare, but do occur. By handling false positives well without getting upset, you are helping this bot catch almost half of all vandalism on Knowledge and keep the wiki clean for all of us.
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network. It is essentially a copy of the
Knowledge RC feed, but with ClueBot NG's analysis data added. It includes everything the Knowledge RC feed does, with the addition of the ClueBot NG score and whether it was reverted or not. Format is
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parts of the dataset (currently, the ones that are human-reviewed from the review interface) are used for this calculation. This ensures that all statistics given here are accurate, and that false positives will not exceed the given rate.
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The thing with ClueBot NG, is that every time I try to manually revert an edit, ClueBot NG immediately beats me. That's how fast CBNG is; vandalism comes in, instantly reverted. Here's some transistors to keep you running.
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Currently, there's also a separate
Bayesian classifier that works in units of 2-word phrases. We may add even more Bayesian classifiers in the future that work in different units of words, or words in different contexts.
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quite a few false positives, because many of the heuristics have legitimate uses in some contexts, and only about a 5% to 10% vandalism catch rate, because most vandalism cannot be detected by these simple heuristics.
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False positives with ClueBot NG are (essentially) inevitable. For it to be effective at catching a great deal of vandalism, a few constructive (or at least, well-intentioned) edits are caught. There are
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This differs from a simple list of blacklisted words in that word weights are exactly determined to be optimal, and there's also a large "whitelist" of words, also with optimal weights, that contributes.
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The output of the neural network is used as the main vandalism score for ClueBot NG. As with other machine-learning techniques, the score's accuracy depends on the training dataset size and accuracy.
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edit, it does not mean that your edit is bad, or even substandard — it's just a random error in the bot's classification, just like email spam filters sometimes incorrectly classify messages as spam.
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False positive rate is set by a human, and the bot stays at or below that false positive rate, while catching as much vandalism as possible. The false positive rate is not fixed, but is adjustable.
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for each added word in an edit. The probabilities are combined in such a way that not only words common in vandalism are used, but also words that are uncommon in vandalism can reduce the score.
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This is a red gift card that the bots can use at the
Barnstar Shop. Feel free to buy any barnstars there, and maybe even give them to other users! (But please don’t award yourself some.)
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The first two of these rarely reduce catch rate, but both prevent a fair number of false positives. Note: The false positive rate (and catch rate) are calculated in the core,
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a random sampling of edits, reviewed and classified by volunteers. More thorough instructions on how to use the interface, and the interface itself, are at the
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Currently, the trial dataset used to generate these statistics is a random sampling of edits, each reviewed by at least two humans, so statistics are accurate.
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We appreciate all you do with protecting the integrity of
Knowledge and regulating articles so that users do not have to directly engage with vandals as much!
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edits incorrectly classified as vandalism. A threshold is calculated to have a false positive rate at or below this percentage, while maximizing catch rate.
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What!? This bot is faster than any other bot in Knowledge. Excellent job at reverting vandalism, Cluebot NG. You have made almost everybody's lives easier. —
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results from the Bayesian classifiers. Each statistic has to be scaled to a number between zero and one before being input to the neural network.
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Edit Count — If a user has more than a threshold number of edits, and fewer than a threshold percentage of warnings, the edit is not reverted.
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ClueBot NG uses a combination of different detection methods which use machine learning at their core. These are described below.
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An Anti-vandalism barnstar and a half barn star for ClueBot NG, for the bot’s work on fighting vandalism and making over
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If ClueBot NG weren’t here, we won’t revert vandalism as much as it does. Thank you for all the edits that you’ve made.
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User Whitelist — If an edit made by a user that is in a whitelist is classified as vandalism, the edit is not reverted.
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I haven't seen much of your work, but you have been doing well it seems. Keep up the good work you wonderful bot boy!
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ClueBot NG maintains an IRC-based feed of its data, primary intended for use by other automated tools, located at
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ClueBot NG is not a person, it is an automatic robot that tries to detect vandalism and keep Knowledge clean. A
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Selecting a false positive rate of 0.25% (old setting), the bot catches approximately 55% of all vandalism.
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For those that help with and contribute to the false positive interface, a user box is available for you:
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1313:(đź“–đź’ž) and hopefully this one has made your day more powerful. It is the power source best preferred by
1262:(đź“–đź’ž) and hopefully this one has made your day more powerful. It is the power source best preferred by
904:— The bot shell (Knowledge interface) is written in PHP, and shares some code with the original ClueBot.
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1RR — The same user/page combination is not reverted more than once per day, unless the page is on the
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For reverting vandalism on a full time basis and thanks to it's creators for their hard work on it.
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Currently writing a dedicated wiki markup parser for more accurate markup-context-specific metrics.
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1193:(đź“–đź’ž) and hopefully this one has made your day more efficient. It is the drink best preferred by
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The reason false positives are necessary is due to how the bot works. It uses a complex internal
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For those that help with and contribute to the review interface, a user box is available for you:
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1131:(đź“–đź’ž) and hopefully this one has made your day more efficient. It is the food best preferred by
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Selecting a threshold to optimize total accuracy, the bot correctly classifies over 90% of edits.
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1135:. 🤖 Spread the WikiLove by giving someone else transistors, whether it be someone you have had
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1317:. 🤖 Spread the WikiLove by giving someone else batteries, whether it be someone you have had
1266:. 🤖 Spread the WikiLove by giving someone else batteries, whether it be someone you have had
1197:. 🤖 Spread the WikiLove by giving someone else motor oil, whether it be someone you have had
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The main component of the ClueBot NG vandalism detection algorithm is the neural network. An
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Extended statistics on contributors, including edit review counts and accuracy, are available
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459:) — For writing the original dataset downloader code and providing the original dataset.
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The exact statistics change and improve frequently as we update the bot. Currently:
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the bot's interface to Knowledge and dataset review interface should be directed to
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916:— A few scripts to make it easier to train and maintain the bot are Bash scripts.
910:— The dataset review interface is written in Java using the Google App framework.
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To report a false positive, or to see a full list of all false positives, see
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is when an edit that is not vandalism is incorrectly classified as vandalism.
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post-processing filters. This means that actual false positive rate will be
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edits that would be extremely tedious to do manually, in accordance with the
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This is for your valuable efforts for reverting and protecting enwiki from
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This has been fixed and the bot retrained. It's now working much better.
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Selecting a threshold to hold false positives at a maximal rate of 0.1%
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Administrators: if this bot is malfunctioning or causing harm, please
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the core engine, algorithms, and configuration should be directed to
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Currently changing logic that determines the end result for an edit.
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537:) — For helping with minor issues, testing, and people-handling.
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The bot is not biased against you, your edit, or your viewpoint
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Questions, comments, contributions, and suggestions regarding:
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Every user who has contributed to the dataset review interface.
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ClueBot NG uses a completely different method for classifying
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847:(No existing alternative parsers are complete or fast enough)
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The source code for the bot is public, and can be found on
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than stated false positive, often by a significant factor.
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edit line \003 # score # reason # Reverted or Not reverted
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76:. The bot is approved and currently active – the relevant
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One of the top three most valued bots running on enwiki.
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433:) — wrote and maintains the Knowledge interface code and
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edits! Thanks for the hard work on reverting vandalism!
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Currently getting more data from the review interface.
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Everyone who has made a helpful and useful suggestion.
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1328:}} to someone's talk page with a friendly message!
1277:}} to someone's talk page with a friendly message!
1208:}} to someone's talk page with a friendly message!
1150:}} to someone's talk page with a friendly message!
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ClueBot NG aids in Operation Enduring Encyclopedia.
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1783:Such material is not meant to be taken seriously.
585:the bot's original dataset should be directed to
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354:Administrators may turn the bot off by changing
1776:material that is kept because it is considered
1146:Spread the goodness of transistors by adding {{
857:Code to import edits into database is finished.
158:Knowledge:Administrators' noticeboard/Incidents
141:Use this button if the bot is malfunctioning. (
1901:Knowledge bots with PHP source code published
1324:Spread the goodness of batteries by adding {{
1273:Spread the goodness of batteries by adding {{
1204:Spread the goodness of motor oil by adding {{
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1896:Knowledge bots which are exclusion compliant
898:— The core is written in C/C++ from scratch.
873:generation so we can regenerate the dataset.
801:of these are mandated by Knowledge policy.
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475:) — For providing server resources at
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1139:with in the past or a good friend.
1057:{{User:ClueBot NG/Report User Box}}
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1321:with in the past or a good friend.
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664:{{User:ClueBot NG/Review User Box}}
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1562:A gift card from the Barnstar Shop
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646:to help automatically mass revert
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964:Information About False Positives
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385:that tries to detect and revert
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1388:I like beer and you should too
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760:Artificial Neural Network (ANN)
350:Administrator emergency shutoff
1035:reviews false positive reports
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64:It is used to make repetitive
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1746:SuperTurboChampionshipEdition
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1254:HelloHamburger has given you
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714:Vandalism Detection Algorithm
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412:) — wrote and maintains the
313:Emergency shutoff-compliant?
124:Emergency bot shutoff button
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1666:The Anti-Vandalism Barnstar
1596:The Anti-Vandalism Barnstar
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1418:The Anti-Vandalism Barnstar
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389:quickly and automatically.
324:ClueBot NG is run from the
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1868:Talk page for all ClueBots
1738:The Hard Worker's Barnstar
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1460:To ClueBot NG, for making
702:Frequently Asked Questions
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1834:ClueBot NG/Anti-vandalism
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1148:subst:Transistors for you
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1326:subst:Batteries for you
1275:subst:Batteries for you
1206:subst:Motor oil for you
831:Development News/Status
796:Post-Processing Filters
730:Machine Learning Basics
416:and core configuration.
398:Christopher Breneman —
368:exclusion compliant bot
150:Non-administrators can
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1630:A robot for ClueBot NG
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1127:! Transistors promote
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945:#wikipedia-en-cbngfeed
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1524:The Multiple Barnstar
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1189:! Motor oil promotes
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776:Threshold Calculation
640:reviews dataset edits
414:core detection engine
381:is an anti-vandalism
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1454:The Special Barnstar
1309:! Batteries promote
1258:! Batteries promote
939:ClueBot NG IRC Feeds
739:Bayesian Classifiers
265:Programming language
256:Automatic or manual?
78:request for approval
1860:ClueBot III/Archive
1774:This page contains
1223:The Useful AI Award
441:Special thanks to:
362:Exclusion compliant
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303:Exclusion compliant
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358:to 'False'.
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80:can be seen
46:operated by
40:user account
37:
1125:transistors
949:Libera Chat
927:Source Code
836:Core Engine
251:Continually
144:direct link
1880:Categories
1431:PATH SLOPU
1390:C.carleigh
1319:robot wars
1268:robot wars
1199:robot wars
1157:2NumForIce
1137:robot wars
1039:ClueBot NG
1033:This user
668:Statistics
644:ClueBot NG
638:This user
553:Crispy1989
437:interface.
400:Crispy1989
379:ClueBot NG
212:Rich Smith
166:ClueBot NG
74:bot policy
48:Rich Smith
1827:ClueBots
1462:5 million
1426:Vandalism
1333:TK421bsod
1307:batteries
1256:batteries
1187:motor oil
1100:Cremastra
1043:vandalism
992:algorithm
886:Languages
720:vandalism
648:vandalism
511:H3llkn0wz
387:vandalism
356:this page
295:more info
259:Automatic
66:automated
1778:humorous
1570:Porkchop
1536:Porkchop
1311:WikiLove
1260:WikiLove
1191:WikiLove
1129:WikiLove
1014:User box
984:very few
706:See the
597:contribs
580:contribs
563:contribs
535:contribs
523:) &
521:contribs
507:contribs
493:contribs
473:contribs
457:contribs
431:contribs
410:contribs
223:Approved
208:Operator
185:contribs
106:Shortcut
91:block it
1363:Dino245
947:on the
587:Tim1357
525:b930913
477:ClueNet
447:Tim1357
374:Summary
238:Flagged
114:WP:CBNG
1794:Praise
1683:WHEELZ
1066:Awards
933:github
920:Python
806:before
497:SnoFox
445:Tim —
435:review
289:, and
283:Python
153:report
1168:edits
826:list.
228:Yes,
42:is a
38:This
1750:talk
1704:RIP!
1642:talk
1638:Iggy
1608:talk
1470:talk
1394:talk
1367:talk
1337:talk
1315:bots
1289:talk
1264:bots
1235:talk
1195:bots
1133:bots
1104:talk
1037:for
1008:here
914:Bash
908:Java
810:less
642:for
618:here
591:talk
574:talk
557:talk
529:talk
515:talk
501:talk
487:talk
467:talk
451:talk
425:talk
404:talk
393:Team
291:Java
287:Bash
243:Yes.
230:BRFA
181:talk
82:here
60:talk
54:)and
52:talk
1744::)
1676:Bey
1573:Jr.
1539:Jr.
902:PHP
896:C++
708:FAQ
509:),
495:),
383:bot
316:Yes
308:Yes
279:PHP
275:C++
267:(s)
173:bot
68:or
44:bot
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1717:đź“ť
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