Decoding Digital Influence: The Role of Social Media Behavior in Scientific Stratification Through Logistic Attribution Method
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911964703227904 |
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| author | Yue, Yang |
| author_facet | Yue, Yang |
| contents | Scientific social stratification is a classic theme in the sociology of science. The deep integration of social media has bridged the gap between scientometrics and sociology of science. This study comprehensively analyzes the impact of social media on scientific stratification and mobility, delving into the complex interplay between academic status and social media activity in the digital age. [Research Method] Innovatively, this paper employs An Explainable Logistic Attribution Analysis from a meso-level perspective to explore the correlation between social media behaviors and scientific social stratification. It examines the impact of scientists' use of social media in the digital age on scientific stratification and mobility, uniquely combining statistical methods with machine learning. This fusion effectively integrates hypothesis testing with a substantive interpretation of the contribution of independent variables to the model. [Research Conclusion] Empirical evidence demonstrates that social media promotes stratification and mobility within the scientific community, revealing a nuanced and non-linear facilitation mechanism. Social media activities positively impact scientists' status within the scientific social hierarchy to a certain extent, but beyond a specific threshold, this impact turns negative. It shows that the advent of social media has opened new channels for academic influence, transcending the limitations of traditional academic publishing, and prompting changes in scientific stratification. Additionally, the study acknowledges the limitations of its experimental design and suggests future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15854 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decoding Digital Influence: The Role of Social Media Behavior in Scientific Stratification Through Logistic Attribution Method Yue, Yang Computers and Society Digital Libraries Methodology Machine Learning Scientific social stratification is a classic theme in the sociology of science. The deep integration of social media has bridged the gap between scientometrics and sociology of science. This study comprehensively analyzes the impact of social media on scientific stratification and mobility, delving into the complex interplay between academic status and social media activity in the digital age. [Research Method] Innovatively, this paper employs An Explainable Logistic Attribution Analysis from a meso-level perspective to explore the correlation between social media behaviors and scientific social stratification. It examines the impact of scientists' use of social media in the digital age on scientific stratification and mobility, uniquely combining statistical methods with machine learning. This fusion effectively integrates hypothesis testing with a substantive interpretation of the contribution of independent variables to the model. [Research Conclusion] Empirical evidence demonstrates that social media promotes stratification and mobility within the scientific community, revealing a nuanced and non-linear facilitation mechanism. Social media activities positively impact scientists' status within the scientific social hierarchy to a certain extent, but beyond a specific threshold, this impact turns negative. It shows that the advent of social media has opened new channels for academic influence, transcending the limitations of traditional academic publishing, and prompting changes in scientific stratification. Additionally, the study acknowledges the limitations of its experimental design and suggests future research directions. |
| title | Decoding Digital Influence: The Role of Social Media Behavior in Scientific Stratification Through Logistic Attribution Method |
| topic | Computers and Society Digital Libraries Methodology Machine Learning |
| url | https://arxiv.org/abs/2407.15854 |