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Status, identity, and language: A study of issue discussions in GitHub.

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Area of Science:

  • Computer Science
  • Sociolinguistics
  • Social Computing

Background:

  • Open source software (OSS) projects involve complex social networks and vast discussions.
  • Participant overload makes it difficult to identify relevant individuals for collaboration.
  • Tagging is a practice to focus discussions but requires knowing who to tag.

Purpose of the Study:

  • To investigate how textual content in OSS discussions reveals speaker status and identity.
  • To model the identification of relevant people using deep learning techniques.
  • To develop tools that aid participants in finding key individuals within OSS projects.

Main Methods:

  • Socio-linguistic analysis of textual content in OSS discussions.
  • Application of deep learning models to understand social dynamics and identify influential participants.
  • Development of assistive tools for participant and discussion relevance.

Main Results:

  • Textual analysis effectively reveals social status and identity within OSS communication.
  • Deep learning models can successfully model the socio-linguistic phenomena observed.
  • Demonstrated the utility of developed tools in identifying relevant people for discussions.

Conclusions:

  • Understanding the socio-linguistics of OSS communication is crucial for effective collaboration.
  • Deep learning offers a powerful approach to analyze and navigate complex OSS social networks.
  • Tools based on these methods can significantly improve participant efficiency and project success.