How many ways to use CiteSpace? A study of user interactive events over 14 months

Abstract

Using visual analytic systems effectively may incur a steep learning curve for users, especially for those who have little prior knowledge of either using the tool or accomplishing analytic tasks. How do users deal with a steep learning curve over time? Are there particularly problematic aspects of an analytic process? In this article we investigate these questions through an integrative study of the use of CiteSpace—a visual analytic tool for finding trends and patterns in scientific literature. In particular, we analyze millions of interactive events in logs generated by users worldwide over a 14‐month period. The key findings are: (i) three levels of proficiency are identified, namely, level 1: low proficiency, level 2: intermediate proficiency, and level 3: high proficiency, and (ii) behavioral patterns at level 3 are resulted from a more engaging interaction with the system, involving a wider variety of events and being characterized by longer state transition paths, whereas behavioral patterns at levels 1 and 2 seem to focus on learning how to use the tool. This study contributes to the development and evaluation of visual analytic systems in realistic settings and provides a valuable addition to the study of interactive visual analytic processes.

The Impact of Risk Perception on Social Distancing during the COVID-19 Pandemic in China

“…Then, using Citespace software, which is designed as a tool for progressive knowledge domain visualization [ 19 ], the co-occurrence matrix of keywords was calculated and visualized, as shown in Figure 1 . The size of the keywords presents the frequency of co-occurrence and the connection shows the significance of co-occurrence [ 20 ].…”

A comparative study of international and Chinese public health emergency management from the perspective of knowledge domains mapping

“…CiteSpace was a free Java-based application found by Chaomei Chen to analyze the potential knowledge contained in the scientific literature. It has been widely adopted for scientometric analysis in various scientific fields [ 29 ], and has achieved excellent results [ 30 , 31 ]. The parameters of CiteSpace for this study were set as follows: time slicing (1991–2018, 2003–2018, respectively), years per slice (1), term source (all selection), node type (author, institution, keyword, for Chinese and international data; country for international data), selection criteria (top 50), and visualization (cluster view-static, show merged network).…”

Visualizing Sustainability Research in Business and Management (1990–2019) and Emerging Topics: A Large-Scale Bibliometric Analysis

“…The change in color from cold to warm tones indicates time changes from early to the more recent. Other nodes with red tree rings are references with citation burst, that is, cited references were suddenly added in the short term [50].…”