Skip to content
J·BJon BishopIndependent growth marketer

Platforms · Empirical study (abstract-only)

An empirical study of software ecosystem related tweets by npm maintainers

PeerJ Computer Science · 2024

Syful Islam, Yusuf Sulistyo Nugroho, Chy. Md. Shahrear, Nuhash Wahed, Dedi Gunawan, Endang Wahyu Pamungkas, Mohammed Humayun Kabir, Yogiek Indra Kurniawan, Md. Kamal Uddin

Summary

The npm ecosystem is crucial for the JavaScript community and its development is significantly influenced by the opinions and feedback of npm maintainers. Many software ecosystem maintainers have utilized social media, such as Twitter, to share community-related information and their views. However, the communication between npm maintainers via Twitter in terms of topics, nature, and sentiment have not been analyzed. This study conducts an empirical analysis of tweets by npm maintainers related.

For your marketing

Possible founder application

Use the study’s platform, complementor, API, ecosystem, open-source, or standards implications to guide governance, partner incentives, integration priorities, and ecosystem experimentation; validate in the founder’s market.

Reported result

What the study found

The npm ecosystem is crucial for the JavaScript community and its development is significantly influenced by the opinions and feedback of npm maintainers. Many software ecosystem maintainers have utilized social media, such as Twitter, to share community-related information and their views. However, the communication between npm maintainers via Twitter in terms of topics, nature, and sentiment have not been analyzed. This study conducts an empirical analysis of tweets by npm maintainers related.

Research question

What the authors investigated

How does the study’s stated design explain its reported outcome in this setting?

Design

Empirical study (abstract-only)

Sample and setting

Abstract-only; sample/context details were not reported in the fetched metadata.

Scope and transfer

Limitations to keep in view

Abstract/metadata-only evidence; detailed sampling, measures, causal identification, and external-validity limits require the full paper.

Research sources

abstract_only

Prefer to write? Send Jon a message.