Data Journalism and Fact-Checking Research: Global Bibliometric Trends and Adoption in Indonesia 26 27
DOI:
https://doi.org/10.26623/themessenger.v18i2.13259Keywords:
Comparative Bibliometric, Data Journalism, Fact-Checking, Thematic Mapping, Global SouthAbstract
Purpose: This study aimed to map the intellectual structure of global research on data journalism and fact-checking and analyzed its specific adoption patterns in Indonesia to understand how global trends translated into the Global South context.
Methods: Adopting a quantitative bibliometric approach, data were retrieved from the Scopus database covering the last decade. The study utilized Biblioshiny software, selected for its integrated statistical workflow and superior capability in comparative thematic mapping, to visualize and compare co-occurrence and co-citation networks between the global dataset and the Indonesian adoption dataset.
Findings: Global results revealed an exponential growth trajectory driven by the interdisciplinary convergence of journalism studies and computer science, with Natural Language Processing and Pandemic emerging as dominant motor themes. Conversely, the Indonesian dataset indicated a unique, reactive adoption pattern with a sharp acceleration in publications during 2016–2025. Unlike the global trend toward automated detection, Indonesian research prioritized Social Media as the central problem context and Human collaboration as the primary solution, reflecting a sociotechnical adaptation to local resource constraints.
Originality: This study provided new theoretical insights into journalism technology adoption in the Global South by conceptualizing the methodological divide between global computational trends and local human-centric practices. It offered a non-Western perspective on digital media evolution and suggested future empirical research on human-machine collaboration.
Downloads
References
Abramo, G., D’Angelo, C. A., & Di Costa, F. (2019). Authorship analysis of specialized vs diversified research output. Journal of Informetrics, 13(2), 564–573. https://doi.org/10.1016/j.joi.2019.03.004
Allcott, H., & Gentzkow, M. (2017). Social Media and Fake News in the 2016 Election. Journal of Economic Perspectives, 31(2), 211–236. https://doi.org/10.1257/jep.31.2.211
Appelgren, E. (2022). Media Management During COVID-19: Behavior of Swedish Media Leaders in Times of Crisis. Journalism Studies, 23(5–6), 722–739. https://doi.org/10.1080/1461670X.2021.1939106
Appelgren, E., Lindén, C.-G., & van Dalen, A. (2019). Data Journalism Research: Studying a Maturing Field across Journalistic Cultures, Media Markets and Political Environments. Digital Journalism, 7(9), 1191–1199. https://doi.org/10.1080/21670811.2019.1685899
Bovet, A., & Makse, H. A. (2019). Influence of fake news in Twitter during the 2016 US presidential election. Nature Communications, 10(1), 7. https://doi.org/10.1038/s41467-018-07761-2
Cazzamatta, R. (2025). Fact-Checkers as New Journalistic Mediators: News Agencies’ Verification Units and Platform Dynamics | Article | Media and Communication. https://www.cogitatiopress.com/mediaandcommunication/article/view/9867
Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A. (2020). The COVID-19 social media infodemic. Scientific Reports, 10(1), 16598. https://doi.org/10.1038/s41598-020-73510-5
de-Lima-Santos, M.-F., & Mesquita, L. (2021). Data Journalism Beyond Technological Determinism. Journalism Studies, 22(11), 1416–1435. https://doi.org/10.1080/1461670X.2021.1944279
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Ekström, M., Lewis, S. C., & Westlund, O. (2020). Epistemologies of digital journalism and the study of misinformation. New Media & Society, 22(2), 205–212. https://doi.org/10.1177/1461444819856914
Graves, L. (2016). Deciding what’s true: The rise of political fact-checking in American journalism. New York : Columbia University Press.
Haque, M. M., Yousuf, M., Arman, Z., Rony, M. M. U., Alam, A. S., Hasan, K. M., Islam, M. K., & Hassan, N. (2018). Fact-checking Initiatives in Bangladesh, India, and Nepal: A Study of User Engagement and Challenges (arXiv:1811.01806). arXiv. https://doi.org/10.48550/arXiv.1811.01806
Heravi, B. R., & Lorenz, M. (2020). Data Journalism Practices Globally: Skills, Education, Opportunities, and Values. Journalism and Media, 1(1), 26–40. https://doi.org/10.3390/journalmedia1010003
Humprecht, E., Esser, F., Aelst, P. V., Staender, A., & Morosoli, S. (2023). The sharing of disinformation in cross-national comparison: Analyzing patterns of resilience. Information, Communication & Society, 26(7), 1342–1362. https://doi.org/10.1080/1369118X.2021.2006744
Juditha, C. (2018). Hoax Communication Interactivity in Social Media and Anticipation (Interaksi Komunikasi Hoax Di Media Sosial Serta Antisipasinya). Pekommas, 3(1), 261723. https://doi.org/10.30818/jpkm.2018.2030104
Lazer, D. M. J., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., Margetts, H., Nelson, A., Salganik, M. J., Strohmaier, M., Vespignani, A., & Wagner, C. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060–1062. https://doi.org/10.1126/science.aaz8170
Morini, F. (2025). Different yet complementary: A systematic literature review on data journalism in visualization research and journalism studies. Journalism, 26(2), 425–444. https://doi.org/10.1177/14648849241237897
Mutsvairo, B., Bebawi, S., & Borges-Rey, E. (Eds.). (2019). Data Journalism in the Global South. Springer International Publishing. https://doi.org/10.1007/978-3-030-25177-2
Nurlatifah, M. (2021). Fact-Checking dan Jurnalisme Kolaboratif pada Platform Media Online. Jurnal ILMU KOMUNIKASI, 18(1), 67–86. https://doi.org/10.24002/jik.v18i1.1871
Opdahl, A. L., Tessem, B., Dang-Nguyen, D.-T., Motta, E., Setty, V., Throndsen, E., Tverberg, A., & Trattner, C. (2023). Trustworthy journalism through AI. Data & Knowledge Engineering, 146, 102182. https://doi.org/10.1016/j.datak.2023.102182
Passas, I. (2024). Bibliometric Analysis: The Main Steps. Encyclopedia, 4(2), 1014–1025. https://doi.org/10.3390/encyclopedia4020065
Peng, T.-Q., Liang, H., & Zhu, J. J. H. (2019). Introducing computational social science for Asia-Pacific communication research. Asian Journal of Communication, 29(3), 205–216. https://doi.org/10.1080/01292986.2019.1602911
Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021). Shifting attention to accuracy can reduce misinformation online. Nature, 592(7855), 590–595. https://doi.org/10.1038/s41586-021-03344-2
Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, The Cognitive Science of Political Thought, 188, 39–50. https://doi.org/10.1016/j.cognition.2018.06.011
Pooley, J. (2019). Review of Communication: A Post-Discipline, by Silvio R. Waisbord (Xcqa3_v1). MediArXiv. https://doi.org/10.33767/osf.io/xcqa3
Posetti, J., & Matthews, A. (2018). A short guide to the history of ’fake news’ and disinformation. ternational Center for Journalists.
Ruiz-Real, J. L., Uribe-Toril, J., De Pablo Valenciano, J., & Gázquez-Abad, J. C. (2018). Worldwide Research on Circular Economy and Environment: A Bibliometric Analysis. International Journal of Environmental Research and Public Health, 15(12), 2699. https://doi.org/10.3390/ijerph15122699
Shen, Y., Liu, Q., Guo, N., Yuan, J., & Yang, Y. (2023). Fake News Detection on Social Networks: A Survey. Applied Sciences, 13(21), 11877. https://doi.org/10.3390/app132111877
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22–36. https://doi.org/10.1145/3137597.3137600
Shu, K., Wang, S., Lee, D., & Liu, H. (2020). Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements. In K. Shu, S. Wang, D. Lee, & H. Liu (Eds.), Disinformation, Misinformation, and Fake News in Social Media: Emerging Research Challenges and Opportunities (pp. 1–19). Springer International Publishing. https://doi.org/10.1007/978-3-030-42699-6_1
Shu, K., Wang, S., & Liu, H. (2019). Beyond News Contents: The Role of Social Context for Fake News Detection. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM ’19, 312–320. https://doi.org/10.1145/3289600.3290994
Singh, C. K., Filho, D. V., Jolad, S., & O’Neale, D. R. J. (2020). Evolution of interdependent co-authorship and citation networks. Scientometrics, 125(1), 385–404. https://doi.org/10.1007/s11192-020-03616-0
Stalph, F. (2018). Classifying Data Journalism: A content analysis of daily data-driven stories. Journalism Practice, 12(10), 1332–1350. https://doi.org/10.1080/17512786.2017.1386583
Syarrafah, M., Satibi, I. F., Sembilu, N., & Mukhlis, I. R. (2025). Memetakan Lanskap Akademik tentang Fake News di Indonesia: Kajian Bibliometrik. MUKASI: Jurnal Ilmu Komunikasi, 4(2), 380–393. https://doi.org/10.54259/mukasi.v4i2.4376
Tandoc Jr., E. C., Jenkins, J., & Craft, S. (2019). Fake News as a Critical Incident in Journalism. Journalism Practice, 13(6), 673–689. https://doi.org/10.1080/17512786.2018.1562958
Tandoc Jr., E. C., Thomas, R. J., & Bishop, L. (2021). What Is (Fake) News? Analyzing News Values (and More) in Fake Stories, Media and Communication, 9(1). https://doi.org/10.17645/mac.v9i1.3331
Tejedor, S., Romero-Rodríguez, L. M., & Gracia-Villar, M. (2024). Unveiling the truth: A systematic review of fact-checking and fake news research in social sciences. Online Journal of Communication and Media Technologies, 14(2), e202427. https://doi.org/10.30935/ojcmt/14455
Usher, N. (2020). News cartography and epistemic authority in the era of big data: Journalists as map-makers, map-users, and map-subjects. New Media & Society, 22(2), 247–263. https://doi.org/10.1177/1461444819856909
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559
Waisbord, S. (2019). The vulnerabilities of journalism. Journalism, 20(1), 210–213. https://doi.org/10.1177/1464884918809283
Yang, J., & Tian, Y. (2021). “Others are more vulnerable to fake news than I Am”: Third-person effect of COVID-19 fake news on social media users. Computers in Human Behavior, 125, 106950. https://doi.org/10.1016/j.chb.2021.106950
Zhang, X., & Li, W. (2020). From Social Media with News: Journalists’ Social Media Use for Sourcing and Verification. Journalism Practice, 14(10), 1193–1210. https://doi.org/10.1080/17512786.2019.1689372
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Erai Tsabbita Pranastia, Rully Khairul Anwar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Notice
We declare that:
1. This paper has not been published in the same form elsewhere.
2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.
3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable.
Retained Rights/Terms and Conditions
1. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors may reproduce or authorize others to reproduce the work or derivative works for the authors personal use or for company use.
3. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
