View source for In the company of CluebotNG

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| Type: || Algoliterary work
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| Datasets: || Wikipedia edits
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| Technique: || Supervised machine learning, Naive Bayesian classifiers
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| Developed by: || User:Cobi, User:Crispy1989, Cristina Cochior
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Wikipedia relies on machine assistance when it comes to maintenance. One of its most active applications is [https://en.wikipedia.org/wiki/User:ClueBot_NG CluebotNG], an anti-vandalism bot operating on the English Wikipedia since December 2010. 

CluebotNG uses a series of different Bayesian classifiers, which measure word weights to attribute a likeliness score for an edit to be considered vandalist. The results of this are fed to an artificial neural network which further allocates the edits a number between 0 and 1, where one represents a 100% chance that an edit is ill intended.

In order to establish the point from which a contribution is considered vandalist, a threshold needs to be chosen by the operator of the application. This is calculated in relation to the false positives rate that it generates: the lower a threshold the higher the number of [https://en.wikipedia.org/wiki/False_positives_and_false_negatives false positives]. The algorithm catches most of the vandalism on the platform when it makes the most mistakes. The threshold value for CluebotNG has caused a lot of debate on Wikipedia, where a balance between high efficiency is set against the number of editors who are wrongly accused. 

If a false positive occurs, it is up to the code maintainer to examine it, add it to a list of exceptions so that the algorithm can learn from the misclassification. This implies constant close attention from the maintainer. 

[[File:Screen_Shot_2017-10-25.png]]

Through a reenactment bot that goes through each of the edits of CluebotNG and displays them on a monitor, the sequential replication of its edits is weaving together a narrative of the nonhuman voices that usually pass unnoticed on media platforms. Each micro-interaction of the bot is intrinsically performed in connection to a human editor, whom the algorithm is policing. As the taxidermic program runs, a sense of body emerges through the time span it would take to get to the end of the bot’s edits.

[[Category:Algoliterary-Encounters]]