Download pdf
Leveraging Data Analytics and Visualization Tools to Uncover In-depth Insights in Journalism: Techniques and Applications
- 1 University of Melbourne
* Author to whom correspondence should be addressed.
Abstract
It's no exaggeration to say that data-driven journalism has taken off in the era of the Internet. Data analytics and visualisation tools help journalists uncover more in-depth insights into their stories and present complex information in a more easily understandable way. There are different ways to collect, analyse and visualise data. Various tools, from Web scraping to sophisticated coding or modelling are used depending on the topic, data type, and objectives the journalist wants to achieve with them. The purpose of this paper is to showcase the techniques and applications in data-driven journalism, and to offer a concise overview of the topic. It is clear from the case studies and technological development presented below that data journalism does have a transformative power in helping to tell better stories and to better engage audiences by offering more context to important issues. Of course, one of the greatest challenges for data journalism is the traditional problem journalists have in general: accuracy, humanity, and context. These are as important as ever in the data age. The purpose of this paper is to showcase the techniques and applications in data-driven journalism, and to offer a concise overview of the topic.
Keywords
data journalism, data analytics, visualization tools, storytelling, audience engagement
[1]. Inastrilla, C. R. A. (2023). Data visualization in the information society. Seminars in Medical Writing and Education, 2.
[2]. Dhiman, B. (2023). Does artificial intelligence help journalists: A boon or bane? Available at SSRN 4401194.
[3]. Lopezosa, C., et al. (2023). Journalistic innovation: How new formats of digital journalism are perceived in the academic literature. Journalism, 24(4), 821-838.
[4]. Nabilah, S. (2023). Pengaruh penggunaan teknologi big data dalam bisnis retail terhadap keputusan konsumen. WriteBox, 1(1).
[5]. Tarigan, I. M., et al. (2023). Understanding social media: Benefits of social media for individuals. Jurnal Pendidikan Tambusai, 7(1), 2317-2322.
[6]. Sadat Lavasani, M., et al. (2023). Big data analytics opportunities for applications in process engineering. Reviews in Chemical Engineering, 39(3), 479-511.
[7]. Khang, A., et al. (Eds.). (2024). Data-driven modelling and predictive analytics in business and finance: Concepts, designs, technologies, and applications. CRC Press.
[8]. Bach, B., et al. (2023). Challenges and opportunities in data visualization education: A call to action. IEEE Transactions on Visualization and Computer Graphics.
[9]. Qaiser, A., et al. (2023). Comparative analysis of ETL tools in big data analytics. Pakistan Journal of Engineering and Technology, 6(1), 7-12.
[10]. Lotfi, F., et al. (2023). Storytelling with image data: A systematic review and comparative analysis of methods and tools. Algorithms, 16(3), 135.
[11]. Sabirova, D., et al. (2024). Algorithms and 3D visualization tools to bring geometric data to life, facilitating immersive experiences and interactive storytelling. E3S Web of Conferences, 548.
Cite this article
Shi,R. (2024). Leveraging Data Analytics and Visualization Tools to Uncover In-depth Insights in Journalism: Techniques and Applications. Advances in Social Behavior Research,10,44-48.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Journal：Advances in Social Behavior Research
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).