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Understanding and predicting the dissemination of scientific papers on social media: a two-step simultaneous equation modeling–artificial neural network approach
Scientometrics ( IF 3.9 ) Pub Date : 2021-06-23 , DOI: 10.1007/s11192-021-04051-5
Yaxue Ma , Zhichao Ba , Yuxiang Zhao , Jin Mao , Gang Li

Social media platforms have had an enormous impact on the dissemination of scientific work and have fared well in covering scientific papers. However, little is known about the general dissemination process from academia to social media and how various factors affect the dissemination of scientific papers at different stages. In this paper, we proposed a two-staged dissemination process to profile the diffusion of scientific papers from academia to social media. A two-step simultaneous equation modeling–artificial neural network approach was adopted to predict the retweet scale of scientific papers on Twitter by combining source-related and content-related factors. The analysis in the field of oncology suggests that the artificial neural network algorithm (ANN) with the input units generated from the simultaneous equation model (3SLS) can predict the retweet scale of scientific papers on Twitter with an accuracy of 78.05%. According to the normalized importance obtained from the ANN, we found that most factors related to the information source play critical roles in promoting the dissemination of scientific papers. The number of first-generation tweets has the most remarkable impact on subsequent dissemination. As for the content-related predictors, tweets attached with more URLs can provide richer information for audiences, thereby increasing the retweet scale of scientific papers. Besides, the influence of research topics on dissemination varies with different audiences. The findings of this study contribute to the literature on the dissemination of scientific papers beyond academia and provide practical implications for scholarly communication.



中文翻译:

理解和预测科学论文在社交媒体上的传播:两步联立方程建模——人工神经网络方法

社交媒体平台对科学工作的传播产生了巨大影响,并且在报道科学论文方面表现良好。然而,人们对从学术界到社交媒体的一般传播过程以及各种因素如何影响不同阶段科学论文的传播知之甚少。在本文中,我们提出了一个两阶段的传播过程来描述科学论文从学术界到社交媒体的传播。采用两步联立方程建模 - 人工神经网络方法,通过结合源相关和内容相关因素来预测 Twitter 上科学论文的转发规模。肿瘤学领域的分析表明,具有联立方程模型(3SLS)生成的输入单元的人工神经网络算法(ANN)可以预测 Twitter 上科学论文的转发规模,准确率为 78.05%。根据从人工神经网络获得的归一化重要性,我们发现与信息源相关的大多数因素在促进科学论文的传播方面起着关键作用。第一代推文的数量对后续传播的影响最为显着。对于内容相关的预测因子,推文附加更多的 URL 可以为受众提供更丰富的信息,从而增加科学论文的转发规模。此外,研究主题对传播的影响因受众不同而不同。

更新日期:2021-07-19
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