Open AccessStatus, identity, and language: A study of issue discussions in GitHub
Author(s) -
Jingxian Liao,
Guowei Yang,
David Kavaler,
Vladimir Filkov,
Prémkumar Dévanbu
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0215059
Subject(s) - task (project management) , identity (music) , perspective (graphical) , value (mathematics) , computer science , data science , open source software , phenomenon , world wide web , software , epistemology , artificial intelligence , engineering , philosophy , physics , machine learning , acoustics , programming language , systems engineering
Successful open source software (OSS) projects comprise freely observable, task-oriented social networks with hundreds or thousands of participants and large amounts of (textual and technical) discussion. The sheer volume of interactions and participants makes it challenging for participants to find relevant tasks, discussions and people. Tagging ( e.g ., @AmySmith) is a socio-technical practice that enables more focused discussion. By tagging important and relevant people, discussions can be advanced more effectively. However, for all but a few insiders, it can be difficult to identify important and/or relevant people. In this paper we study tagging in OSS projects from a socio-linguistics perspective. First we argue that textual content per se reveals a great deal about the status and identity of who is speaking and who is being addressed . Next, we suggest that this phenomenon can be usefully modeled using modern deep-learning methods. Finally, we illustrate the value of these approaches with tools that could assist people to find the important and relevant people for a discussion.