摘要：

【目的】 以“启发-系统”模型为理论基础,探究科普期刊微信推文传播效果的影响因素,为运营实务提供参考。【方法】 通过计算机辅助内容分析引入可读性、信息熵、情感倾向、流行词等变量,从标题特征、发布特征和内容特征对26种科普期刊在半年内发布的4445篇微信推文进行分层回归。【结果】 标题可读性、信息熵和非陈述语气、内容为原创、含流行词、图文并茂的编排、推文位置排在前3对传播广度和深度均有显著影响;内容信息熵以及非夜间时段发布推文对传播广度有显著影响;内容特征对传播效果的解释力度远高于标题与发布特征。【结论】 科普期刊微信运营需凝练标题完善发布策略,深耕内容提高原创比例,并尝试以数据指标驱动适应平台逻辑。

关键词: “启发-系统”模型, 科普期刊, 传播效果, 计算机辅助内容分析, 可读性, 信息熵

Abstract:

[Purposes] Based on the heuristic-systematic model, this study scrutinizes the factors affecting the communication effect of popular science journal articles on WeChat to optimize business practices. [Methods] The paper introduced variables such as readability, information entropy, emotional valence, and popular words by computer-assisted content analysis. Stratified regressions were conducted on 4445 WeChat articles published by 26 popular science journals over six months, considering title features, publication features, and content features. [Findings] Readability, information entropy, and non-declarative mood of the titles, original works, the use of popular terms, and multimedia presentation on the content, as well as the top three positions significantly influence both the breadth and depth of communication effect. Information entropy of the content and non-nighttime publication of articles significantly influence the breadth of communication effect. Content features have much higher explanatory power for communication effect than title and publication features. [Conclusions] The WeChat operation of popular science journals should refine the title, improve the publishing strategy, deepen the content to improve the original proportion, and try to use data indicators to adapt to the platform logic.

Key words: Heuristic-systematic model, Popular science journal, Communication effect, Computer-assisted content analysis, Readability, Information entropy