Supervised Sentiment Classification with CNNs for Diverse SE Datasets

Ram, Achyudh; Nagappan, Meiyappan

doi:10.48550/arxiv.1812.09653

Cited by 1 publication

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“…Future research might nonetheless explore the relevance of a different approach to this measurement problem: train a dedicated machine learning algorithm on human-rated data along each dimension. This would have the benefit of specificity, but is not without costs and challenges (see, e.g., Lin and Luo (2020) or Ram and Nagappan (2018) for a discussion in the context of sentiment analysis).…”

Section: Measures Of Code Qualitymentioning

confidence: 99%

Follow the Leader: Technical and Inspirational Leadership in Open Source Software

Hergueux,

Kessler

2022

Preprint

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We conduct the first comprehensive study of the behavioral factors which predict leader emergence within open source software (OSS) virtual teams. We leverage the full history of developers' interactions with their teammates and projects at github.com between January 2010 and April 2017 (representing about 133 million interactions) to establish that -contrary to a common narrative describing open source as a pure "technical meritocracy" -developers' communication abilities and community building skills are significant predictors of whether they emerge as team leaders. Inspirational communication therefore appears as central to the process of leader emergence in virtual teams, even in a setting like OSS, where technical contributions have often been conceptualized as the sole pathway to gaining community recognition.Those results should be of interest to researchers and practitioners theorizing about OSS in particular and, more generally, leadership in geographically dispersed virtual teams, as well as to online community managers.

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Section: Measures Of Code Qualitymentioning

confidence: 99%