在“量化转向”趋势下,算法新闻的广泛实践引发了学者们的研究热情,形成了丰富的知识图景。本文运用文献计量法对2009年至2020年间的125篇有关算法新闻学的文献资料进行了共现分析和聚类分析。在知识生产上,以美国学者和新闻传播学学者为主导,形成了内部关系紧密、以理工科为背景、美国学者为主体和内部关系松散、以人文学科为背景、欧洲学者为主体的两个合作作者群。在知识演进上,算法新闻学可以划分为初步形成期、全面推进期和精细发展期三个阶段。在知识传播上,新旧结合、多学科交叉的高被引期刊成为重要载体。政治体制、产业实践、公民意识、技术发展等外部社会因素和学者经历、优势学科、资金支持等内部学术因素成为推动西方算法新闻学知识生产的重要力量。

Under the trend of “quantitative turn”, the extensive practice of western algorithmic journalism has aroused the research enthusiasm of scholars and formed a rich knowledge picture. Using bibliometric method, this paper makes co-occurrence analysis and cluster analysis on 125 literatures about western algorithmic journalism from 2009 to 2020. In terms of knowledge production, led by American scholars and Journalism & communication scholars, two cooperative author groups have been formed: one with close internal relations, science and engineering as the background, American scholars as the main body, the other with loose internal relations, humanities as the background and European scholars as the main body. In terms of knowledge evolution, western algorithmic journalism can be divided into three stages: initial formation stage, comprehensive promotion stage and refined development stage. In terms of knowledge dissemination, highly cited journals with the combination of the old and new and interdisciplinary have become an important carrier. Both external social factors (political system, industrial practice, civic awareness, technological development, etc.), and internal academic factors (scholar experience, advantageous disciplines, financial support, etc.) have become important forces to promote the knowledge production of western algorithmic journalism.