Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12–14, 2024, Ningbo, China

Research Article

An Empirical Study of Self-Described Texts of Open Source Projects and Their Attention Levels

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  • @INPROCEEDINGS{10.4108/eai.12-1-2024.2347288,
        author={Xincheng  Wu and Hailin  Shi},
        title={An Empirical Study of Self-Described Texts of Open Source Projects and Their Attention Levels},
        proceedings={Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12--14, 2024, Ningbo, China},
        publisher={EAI},
        proceedings_a={BDEDM},
        year={2024},
        month={6},
        keywords={corporations github open-source project text analysis},
        doi={10.4108/eai.12-1-2024.2347288}
    }
    
  • Xincheng Wu
    Hailin Shi
    Year: 2024
    An Empirical Study of Self-Described Texts of Open Source Projects and Their Attention Levels
    BDEDM
    EAI
    DOI: 10.4108/eai.12-1-2024.2347288
Xincheng Wu1,*, Hailin Shi1
  • 1: SiChuan University
*Contact email: 2021225020104@stu.scu.edu.cn

Abstract

Open source, as a means of open innovation for enterprises, is becoming more and more important in the current business competition. In order to analyze the relationship between the text in the enterprise open source project and the project attention, this paper carries out text mining on the supporting text of the code, and analyzes the regression model through the two dimensions of the amount of knowledge and the way of knowledge presentation. The results of the study proved that the amount of original knowledge and the amount of introduced knowledge are positively correlated with user attention, and the knowledge classification in the presentation of the text can effectively improve user recognition, while there may be a U-shaped relationship between the degree of systematization of the text knowledge and user recognition. This study provides an example for open source project management to extract features through text mining and thus conduct empirical research.