NDLI: Measuring user influence in GitHub: the million follower fallacy

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Measuring user influence in GitHub: the million follower fallacy

Content Provider	ACM Digital Library
Author	Badashian, Ali Sajedi Stroulia, Eleni
Abstract	Influence in social networks has been extensively studied for collaborative-filtering recommendations and marketing purposes. We are interested in the notion of influence in Software Social Networks (SSNs); more specifically, we want to answer the following questions: 1) What does "influence" mean in SSNs? Given the variety of types of interactions supported in these networks and the abundance of centrality-type metrics, what is the nature of the influence captured by these matrics? 2) Are there silos of influence in these platforms or does influence span across thematic boundaries? To investigate these two questions, we first conducted an in-depth comparison of three influence metrics, number of followers, number of forked projects, and number of project watchers in $GitHub^{1}$ (the largest code-sharing and version-control system). Next, we examined how the influence of the most influential software-engineering people in GitHub is spread over different programming languages. Our results indicate (a) that the three influence metrics capture two major characteristics: popularity and content value (code reusability) and (b) that the influence of influentials is spread over more than one programming language, but there is no specific trend toward any two programming languages.
Starting Page	15
Ending Page	21
Page Count	7
File Format	PDF
ISBN	9781450341585
DOI	10.1145/2897659.2897663
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-05-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Social influence Github Software social networks Mining software repositories Programming languages
Content Type	Text
Resource Type	Article

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