@proceedings {1755,
	title = {Mining StackOverflow to Filter out Off-topic IRC Discussion},
	year = {2015},
	month = {05/2015},
	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 offtopic
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, Stack Overflow, youtube},
	attachments = {https://flosshub.org/sites/flosshub.org/files/shaiful-mining_so_0.pdf},
	author = {Shaiful Alam Chowdhury and Hindle, Abram}
}
