Downloads provided by UsageCounts
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 to the software ecosystem to understand their perceptions and opinions better. A dataset of tweets was collected and analyzed using qualitative analysis techniques to identify the topic of tweets, nature, and their sentiments. Our study demonstrates that most tweets belong to the package management category, followed by notifications and community-related information. The most frequently discussed topics among npm maintainers in the package management category are usage scenarios. It appears that the nature of tweets mostly shared by npm maintainers is information, followed by question and answer, respectively. Additionally, the sentiment analysis reveals that npm maintainers express more positive sentiments towards notification and community-related discussion while expressing more neutral opinions towards the package management related discussion. This case study provides valuable insights into the perceptions and opinions of the npm maintainers regarding the software ecosystem and can inform future development and decision making.
FOS: Computer and information sciences, JavaScript, Artificial intelligence, Empirical research, Knowledge management, Core Developer, Network Science and Online Social Networks, Epistemology, Data science, Social media, Sentiment analysis, Engineering, FOS: Electrical engineering, electronic engineering, information engineering, Psychology, Energy Consumption in Mobile Devices and Networks, Electrical and Electronic Engineering, User Participation, Maintainer, QA75.5-76.95, Tweet, Computer science, Computer Science Applications, FOS: Philosophy, ethics and religion, Programming language, World Wide Web, FOS: Psychology, Philosophy, npm ecosystem, Electronic computers. Computer science, Package management, Computer Science, Physical Sciences, Innovation and Collaboration in Open Source Community, Perception, Software, Empirical Studies in Software Engineering, Information Systems, Neuroscience
FOS: Computer and information sciences, JavaScript, Artificial intelligence, Empirical research, Knowledge management, Core Developer, Network Science and Online Social Networks, Epistemology, Data science, Social media, Sentiment analysis, Engineering, FOS: Electrical engineering, electronic engineering, information engineering, Psychology, Energy Consumption in Mobile Devices and Networks, Electrical and Electronic Engineering, User Participation, Maintainer, QA75.5-76.95, Tweet, Computer science, Computer Science Applications, FOS: Philosophy, ethics and religion, Programming language, World Wide Web, FOS: Psychology, Philosophy, npm ecosystem, Electronic computers. Computer science, Package management, Computer Science, Physical Sciences, Innovation and Collaboration in Open Source Community, Perception, Software, Empirical Studies in Software Engineering, Information Systems, Neuroscience
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 25 | |
| downloads | 2 |

Views provided by UsageCounts
Downloads provided by UsageCounts