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| Analysis of Textual Attributes of Scientific Tweets and Their Impact on the Cascading Evolution Trends of Scientific Papers |
| Cao Renmeng1,2, Xu Xiaoke1,2, Wang Xianwen3 |
1.Computational Communication Research Center, Beijing Normal University, Zhuhai 519087
2.School of Journalism and Communication, Beijing Normal University, Beijing 100875
3.WISE Lab, Institute of Science of Science and S&T Management, School of Public Administration and Policy, Dalian University of Technology, Dalian 116024 |
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Abstract Scientific tweets are an important medium for the diffusion of scientific papers on social media. The understanding of the impact of textual attributes of scientific tweets on the diffusion effects of scientific papers helps science communicators in optimizing their strategies, broadening the reach of scientific information, and facilitating academic communication and public engagement. In this study, based on a dataset of over 50,000 papers and 400,000 scientific tweets, we explore the influence of various textual attributes on the cascade propagation dynamics of scientific tweets across three dimensions: tweet content, multimedia elements, and emojis. The results indicate that the use of the highlights of the paper as tweet content, along with incorporation of visual elements such as images, videos, and emojis into the tweets, can significantly enhance the diffusion scope of scientific papers. This effect not only is evident in the initial stages of propagation but also becomes more pronounced in subsequent stages, resulting in a “rich-get-richer” trend, i.e., Matthew effect in cascade diffusion. By introducing a computational communication perspective into altmetrics research, this study provides a deeper and more comprehensive understanding of the diffusion processes and patterns of scientific papers on social media, and thereby uncovers the mechanisms that drive the diffusion of scientific papers.
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Received: 01 January 2025
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