Big-Data Based Analysis for Communication Effect of Science-Technology Public Accounts on Social Media

Jinluan Ren, W. Cao, Bo Li, Lihua Liu, Lin Cai, Ruben Xing
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Abstract

Public accounts on social media have become important channels for information dissemination. Well-designed public social media accounts are vital to better communicate science and technology (S-T) achievements. This article defines the S-T communication concept and proposes the analyzing dimensions. In order to measure the communication effect, this research collected 7,246 articles from S-T public accounts on WeChat. We analysis these massive data incorporating neural network (NN) and multivariate linear regression (MLR) model. The evaluation indicator system of communication effect includes three levels indicators. The research found the following factors affecting the S-T communication effect in different degrees: the number of active fans on Science Technology Public Accounts on Social Media (STPA-SM), locations where the articles are published, the authentication status of STPA-SM, and so on. Finally, the article proposes some strategic suggestions for improving the communication effects of S-T achievements through STPA-SM.
基于大数据的科技公众号社交媒体传播效果分析
社交媒体上的公众账号已经成为信息传播的重要渠道。精心设计的公共社交媒体账户对于更好地传播科技成果至关重要。本文界定了S-T通信的概念,提出了S-T通信的分析维度。为了衡量传播效果,本研究收集了微信S-T公众号的7246篇文章。我们利用神经网络(NN)和多元线性回归(MLR)模型对这些海量数据进行分析。沟通效果评价指标体系包括三个层次的指标。研究发现,科技公众账号(Science Technology Public Accounts on Social Media,简称STPA-SM)的活跃粉丝数量、文章发布地点、STPA-SM的认证状态等因素对科技传播效果有不同程度的影响。最后,本文提出了通过STPA-SM提高科技成果传播效果的策略建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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