@proceedings {1725,
	title = {Mining StackOverflow to Filter out Off-topic IRC Discussion},
	year = {2015},
	month = {05/2015},
	pages = {4 pages},
	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. On- line 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.},
	keywords = {irc, topics},
	url = {http://webdocs.cs.ualberta.ca/~hindle1/2015/shaiful-mining_so.pdf},
	attachments = {https://flosshub.org/sites/flosshub.org/files/shaiful-mining_so.pdf},
	author = {Shaiful Alam Chowdhury and Hindle, Abram}
}
