NDLI: Mining StackOverflow to filter out off-topic IRC discussion

Content Provider	ACM Digital Library
Author	Hindle, Abram Chowdhury, Shaiful Alam
Abstract	Internet Relay Chat (IRC) is a commonly used tool by OpenSource developers. Developers use IRC channels to discuss programming related problems, but much of the discussion is irrelevant and off-topic. Essentially if we treat IRC discussions like email messages, and apply spam filtering, we can try to filter out the spam (the off-topic discussions) from the ham (the programming discussions). Yet we need labelled data that unfortunately takes time to curate. To avoid costly curration in order to filter out off-topic discussions, we need positive and negative data-sources. Online discussion forums, such as StackOverflow, are very effective for solving programming problems. By engaging in open-data, StackOverflow data becomes a powerful source of labelled text regarding programming. This work shows that we can train classifiers using StackOverflow posts as positive examples of on-topic programming discussion. YouTube video comments, notorious for their lack of quality, serve as training set of off-topic discussion. By exploiting these datasets, accurate classifiers can be built, tested and evaluated that require very little effort for end-users to deploy and exploit.
Starting Page	422
Ending Page	425
Page Count	4
File Format	PDF
ISBN	9780769555942
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-16
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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Mining StackOverflow to filter out off-topic IRC discussion