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| A Study of Information Dissemination Network of Altmetrics Top Papers |
| Hao Ruoyang |
| Chinese Academy of Social Sciences Evaluation Studies, Beijing 100732, China |
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Abstract The current study focuses on Altmetrics top papers from 2018 to 2020, aiming at providing an in-depth analysis of the dissemination behaviors of these influential papers on Twitter and the structural characteristics of their information dissemination networks. We first employ the statistical analysis to examine the dissemination behaviors and temporal dynamics of Altmetrics top papers on Twitter. Subsequently, social network analysis methods are used to calculate key parameters of the information dissemination networks, thereby uncovering the underlying mechanisms of information spread. The study results show that the dissemination of Altmetrics top papers on Twitter exhibits pronounced temporal characteristics. The patterns of dissemination volume can be categorized into three main types: declining linear, parabolic, and multi-peaked curves. Notably, for top papers with a declining linear pattern, the change in dissemination volume follows a power-law distribution, with exponents ranging from －2 to －1. This reflects a rapid initial diffusion followed by a gradual attenuation of information spread. Regarding the sources of dissemination, the study finds that the initial spread of top papers is primarily driven by academic users and scholarly journals, who initiate the information flow by mentioning the papers. Subsequently, general users significantly amplify the dissemination through retweeting, thereby broadening the reach and impact of the papers. This indicates that the early stage of information dissemination relies on professional engagement, while the later stage benefits from broader societal participation. Social network analysis reveals that the information dissemination networks of Altmetrics top papers on Twitter exhibit both “small-world” and “core-periphery” structures. These features highlight the complexity and diversity of information dissemination, as well as the interactions among different user groups. Further analysis shows that core users within subgroups—typically researchers or journals—possess high harmonic closeness centrality, playing a dominant role in the rapid and extensive spread of papers. Meanwhile, bridge users within subgroups, characterized by high betweenness centrality, maintain the closest connections with other nodes in the network and play a crucial role in facilitating and controlling information exchange and dissemination. These bridge users act as key connectors, promoting the flow and dissemination of information across the network.The primary innovation of the current study lies in the integration of statistical and social network analysis to quantitatively examine the information dissemination networks and behaviors of Altmetrics top papers on Twitter. By investigating dissemination timelines, sources, network structures, and key users, the study uncovers the underlying patterns of information spread in Twitter’s data ecosystem. The results not only enhance our understanding of academic information dissemination on social media, but also offer new perspectives for interpreting Altmetrics indicators. Future research will further explore the generation and formation processes of underlying Altmetrics data to more comprehensively reveal the nature of these data and their impact on Altmetrics indicators. This will involve a holistic consideration of user behaviors, content characteristics, and network structures, as well as comparative studies of dissemination patterns across different disciplines and platforms. Through deeper analysis, we can better understand the complexity and diversity of academic information dissemination in the digital age and develop more effective strategies for scholarly evaluation and information diffusion.
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Received: 19 September 2024
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2025, Vol. 55 
