Data-driven reporting and investigative peace journalism
Dr. Tamaro J. Green
2021-03-24 20:14:51 viewed: 130
Data-driven reporting may introduce new strategies for investigative peace journalism (De-Arteaga & Boecking, 2019). De-Arteaga and Boecking (2019) apply temporal trends and spatial-temporal anomalies for data-driven investigative reporting. Coddington (2014) examine three data-driven journalism practices of computer-assisted reporting, data journalism, and computational journalism. Coddington (2014) explains computational journalism as technologically oriented journalism which can often employ automation processes and algorithms. Natural language processing enhances language capabilities of data-driven reporting (Wiedemann, Yimam, & Biemann, 2018). Wiedemann et al. (2018) explain natural language processing libraries to support the analysis of heterogeneous data sources in investigative data journalism. Data-driven reporting has many applications such as measuring nationalism by social network analysis (Sari, Ahmad, Hergianasari, Harnita, & Wibowo, 2021).
Coddington, M. (2014). Clarifying journalism’s quantitative turn. Digital Journalism, 3(3), 331-348.
De-Arteaga, M., & Boecking, B. (2019). Killings of social leaders in the Colombian post-conflict: Data analysis for investigative journalism. arXiv e-prints.
Sari, D. K., Ahmad, J., Hergianasari, P., Harnita, P. C., & Wibowo, N. A. (2021). Quantitative study of the cyber-nationalism spreading on Twitter with hashtag Indonesia and Malaysia using social network analysis. Media Watch, 12(1), 161-171.
Wiedemann, G., Yimam, S. M., & Biemann, C. (2018). A multilingual information extraction pipeline for investigative journalism. arXiv e-prints.
Dr. Tamaro Green is a computer science researcher and the founder of TJG Web Services. TJG Web Services, LLC is a consulting firm in the field of information technology. Dr. Green writes on topics of privacy, security, and ethics in information technology and computer science.
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