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Taking the research area of Big Data as a case study, we propose an approachfor exploring how academic topics shift through the interactions amongaudiences across different altmetric sources. Data used is obtained from Web ofScience (WoS) and Altmetric.com, with a focus on Blog, News, Policy, Wikipedia,and Twitter. Author keywords from publications and terms from online events areextracted as the main topics of the publications and the online discussion oftheir audiences at Altmetric. Different measures are applied to determine the(dis)similarities between the topics put forward by the publication authors andthose by the online audiences. Results show that overall there are substantialdifferences between the two sets of topics around Big Data scientific research.The main exception is Twitter, where high-frequency hashtags in tweets have astronger concordance with the author keywords in publications. Among the onlinecommunities, Blogs and News show a strong similarity in the terms commonlyused, while Policy documents and Wikipedia articles exhibit the strongestdissimilarity in considering and interpreting Big Data related research.Specifically, the audiences not only focus on more easy-to-understand academictopics related to social or general issues, but also extend them to a broaderrange of topics in their online discussions. This study lays the foundationsfor further investigations about the role of online audiences in thetransformation of academic topics across altmetric sources, and the degree ofconcern and reception of scholarly contents by online communities.

How do academic topics shift across altmetric sources? A case study of the research area of Big Data