An Interactive Web-Based Dashboard to Examine Trending Topics: Application to Financial Journals

Ngoc Phan (), Nayana Pampapura Madali, Sahar Behpour and Ting Xiao
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Ngoc Phan: Department of Computer Science and Engineering, University of North Texas, 3940 North Elm, Denton, TX 76203-5017, USA
Nayana Pampapura Madali: Department of Information Science, University of North Texas, 3940 North Elm, Denton, TX 76203-5017, USA
Sahar Behpour: Department of Information Science, University of North Texas, 3940 North Elm, Denton, TX 76203-5017, USA
Ting Xiao: Department of Information Science, University of North Texas, 3940 North Elm, Denton, TX 76203-5017, USA

Journal of Information & Knowledge Management (JIKM), 2023, vol. 22, issue 06, 1-28

Abstract: Understanding trends is helpful to identify future behaviours in the field, and the roles of people, places, and institutions in setting those trends. Although traditional clustering strategies can group articles into topics, these techniques do not focus on topics over limited timescales; additionally, even when articles are grouped, the generated results are extensive and difficult to navigate. To address these concerns, we create an interactive dashboard that helps an expert in the field to better understand and quantify trends in their area of research. Trend detection is performed using the time-biased document clustering method. The developed and freely available web application enables users to detect well-defined trending topics by experimenting with various levels of temporal bias — from detecting short-timescale trends to allowing those trends to spread over longer times. Experts can readily drill down into the identified topics to understand their meaning through keywords, example articles, and time range. Overall, the interactive dashboard will allow experts in the field to sift through the vast literature to identify the concepts, people, places, and institutions most critical to the field.

Keywords: Interactive dashboard; journal trends; time biased clustering; data visualization; trend analysis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219649223500405

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