Mining StackOverflow to Filter out Off-topic IRC Discussion

• Author(s) / Creator(s)

  • Chowdhury, S.A.
  • Hindle, Abram

• Internet Relay Chat (IRC) is a commonly used tool by Open Source 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 cur ration in order to filter out off-topic discussions, we need positive and negative data-sources. On-line discussion forums, such as Stack Overflow, are very effective for solving programming problems. By engaging in open-data, Stack Overflow data becomes a powerful source of labelled text regarding programming. This work shows that we can train classifiers using Stack Overflow posts as positive examples of on-topic programming discussion. You Tube 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.

• Date created

  2015

• Subjects / Keywords

  • IRC message filtering
  • Text classification
  • Stackoverflow mining
  • YouTube video comments
  • SVM
  • Naive Bayes

• Type of Item

  Conference/Workshop Presentation

• DOI

  https://doi.org/10.7939/r3-z805-2h57

• License

  Attribution 4.0 International