I know but I Imagine… Algorithmic Stories on the Borderline of Journalism
DOI:
https://doi.org/10.5281/zenodo.15570622Keywords:
Digital Journalism, Cyberjournalism, Media Literacy, University Students, Social Media, Qualitative Analysis.Abstract
Research on algorithmic knowledge has primarily focused on professional users or so-called ordinary people. This segmentation highlights a gap in studying those who fall in between. To fill this gap, we conducted research among journalism students pursuing higher education in journalism who found themselves on the “borderline”: they are no longer “ordinary” users, but are not yet professional specialists. Drawing from latest research we have formulated a theoretical concept of “algorithmic stories on the borderline of journalism”. Through 41 semi-structured interviews with journalism students recruited through snowball sampling, we found that journalism students’ knowledge of AI consisted of imaginaries: ranging from those closely related to the realities of journalism to conspiracy theories. Students perceive knowledge of how social media works as something natural, almost intuitive, coming from many years of experience. On the other hand, journalism studies play a key role in learning the mechanisms of news sites. Among the sources of knowledge, scientific sources are almost absent. In conclusions, we formulate recommendations for efforts to provide future journalists with reliable knowledge about Artificial Intelligence in journalism.
References
Adjin-Tettey, T. D. (2024). From the Classroom to the African Newsroom: How Journalism Education Can Bridge the Gap between the Classroom and the Shrinking Newsroom. African Journalism Studies, 45(3), 178-195. https://doi.org/10.1080/23743670.2024.2321133
Araujo, T., Brosius, A., Goldberg, A. C., Möller, J., & de Vreese, C. (2023). Humans vs. AI: The Role of Trust, Political Attitudes, and Individual Characteristics on Perceptions About Automated Decision Making Across Europe. International Journal of Communication, 17, 6222–6249. http://bit.ly/4a596UZ
Barocas, S., & Nissenbaum, H. (2014). Big Data’s End Run around Anonymity and Consent. In J. Lane, V. Stodden, S. Bender, & H. Nissenbaum (Eds.), Privacy, Big Data, and the Public Good: Frameworks for Engagement (pp. 44-75). Cambridge University Press. https://doi.org/10.1017/CBO9781107590205.004
Bhaskaran, H., Kashyap, G., & Mishra, H. (2024). Teaching Data Journalism: A Systematic Review. Journalism Practice, 18(3), 722-743. https://doi.org/10.1080/17512786.2022.2044888
Bishop, S. (2019). Managing visibility on YouTube through algorithmic gossip. New Media & Society, 21(11-12), 2589-2606. https://doi.org/10.1177/1461444819854731
Braun, V., & Clarke, V. (2021). Thematic Analysis: A Practical Guide. Sage. https://bit.ly/4fKaH3L
Bro, P., Hansen, K. R., & Andersson, R. (2016). Improving Productivity in the Newsroom? Journalism Practice, 10(8), 1005-1018. https://doi.org/10.1080/17512786.2015.1090883
Bucher, T. (2017). The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Information, Communication & Society, 20(1), 30-44. https://doi.org/10.1080/1369118X.2016.1154086
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 2053951715622512. https://doi.org/10.1177/2053951715622512
Castelvecchi, D. (2016). Can we open the black box of AI? Nature News, 538(7623), 20-23. https://doi.org/10.1038/538020a
Christin, A. (2020). The ethnographer and the algorithm: beyond the black box. Theory and Society, 49(5), 897-918. https://doi.org/10.1007/s11186-020-09411-3
Cotter, K. (2019). Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram. New Media & Society, 21(4), 895-913. https://doi.org/10.1177/1461444818815684
Cotter, K. (2024). Practical knowledge of algorithms: The case of BreadTube. New Media & Society, 26(4), 2131-2150. https://doi.org/10.1177/14614448221081802
de Haan, Y., van den, B. E., Goutier, N., Kruikemeier, S., & Lecheler, S. (2022). Invisible Friend or Foe?How Journalists Use and Perceive Algorithmic-Driven Tools in Their Research Process. Digital Journalism, 10(10), 1775-1793. https://doi.org/10.108 0/21670811.2022.2027798
Deuze, M. (2006). Global Journalism Education. Journalism Studies, 7(1), 19-34. https://doi.org/10.1080/14616700500450293
DeVito, M. A., Birnholtz, J., Hancock, J. T., French, M., & Liu, S. (2018). How People Form Folk Theories of Social Media Feeds and What it Means for How We Study Self-Presentation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-12). Association for Computing Machinery. https://doi.org/10.1145/3173574.3173694
DeVito, M. A., Gergle, D., & Birnholtz, J. (2017). “Algorithms ruin everything”: #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 3163-3174). Association for Computing Machinery. https://doi.org/10.1145/3025453.3025659
Dogruel, L. (2021). What is Algorithm Literacy? A Conceptualization and Challenges Regarding its Empirical Measurement. In M. Taddicken & C. Schumann (Eds.), Algorithms and Communication (pp. 67-93). Berlin. https://doi.org/10.48541/dcr.v9.3
Eslami, M., Karahalios, K., Sandvig, C., Vaccaro, K., Rickman, A., Hamilton, K., et al. (2016). First I “like” it, then I hide it: Folk Theories of Social Feeds. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 2371-2382). Association for Computing Machinery. https://doi.org/10.1145/2858036.2858494
French, M., & Hancock, J. (2017). What’s the Folk Theory? Reasoning About Cyber-Social Systems. https://doi.org/10.2139/ssrn.2910571
Gibbs, G. (2018). Analyzing Qualitative Data. SAGE Publications Ltd. https://doi.org/10.4135/9781526441867
Gordon, C., & Lule, J. (2019). Artificial Intelligence and the Introductory Mass Communication Class [Conference]. 5th World Journalism Education Congress, 792-802. https://www.researchgate.net/publication/359090930
Hapek, M. (2024). Asymetria zale?no?ci mi?dzy u?ytkownikiem a organizacj? w dobie sztucznej inteligencji na przyk?adzie wybranych narz?dzi. Zarz?dzanie Mediami, 12(1), 5-25. http://bit.ly/4j8KQFj
Heravi, B. R. (2018). 3Ws of Data Journalism Education. Journalism Practice, 13(3), 349-366. https://doi.org/10.1080/1751278 6.2018.1463167
Hossain, M. S., & Wenger, D. (2024). Has Journalism Education Kept Up With Digital Transformation? Journalism & Mass Communication Educator, 79(2), 129-146. https://doi.org/10.1177/10776958231224038
Jones, B., Jones, R., & Luger, E. (2022). AI ‘Everywhere and Nowhere’: Addressing the AI Intelligibility Problem in Public Service Journalism. Digital Journalism, 10(10), 1731-1755. https://doi.org/10.1080/21670811.2022.2145328
Josephi, B. (2020). Journalism Education. In K. Wahl-Jorgensen & T. Hanitzsch (Eds.), The Handbook of Journalism Studies (pp. 132-145). Routledge. Katzenberger, V. (2024). Mind the gap: re-thinking digital skills in journalism education in Germany. Media Practice and Education, 1-15. https://doi.org/10.1080/25741136.2024.2435805
Kirchhoff, S. (2022). Journalism Education’s Response to the Challenges of Digital Transformation: A Dispositive Analysis of Journalism Training and Education Programs. Journalism Studies, 23(1), 108-130. https://doi.org/10.1080/1461670X.2021.2004555
Low, B., Ehret, C., & Hagh, A. (2025). Algorithmic imaginings and critical digital literacy on #BookTok. New Media & Society, 27(4), 2336-2353. https://doi.org/10.1177/14614448231206466
Marconi, F. (2020). Newsmakers: Artificial Intelligence and the Future of Journalism. Columbia University Press. https://doi.org/10.7312/marc19136-005
Marcus, G. (2018). Innateness, AlphaZero, and Artificial Intelligence. arXiv preprint arXiv:1801.05667. https://doi.org/10.48550/arXiv.1801.05667
Mukhopadhyay, A., & Johar, G. V. (2005). Where There Is a Will, Is There a Way? Effects of Lay Theories of Self-Control on Setting and Keeping Resolutions. Journal of Consumer Research, 31(4), 779-786. https://doi.org/10.1086/426611
Nara, Y. (2024). What You are Seeking is Seeking You: Folk Theories of Personalisation Algorithms Among Internet Users. Journal of Promotional Communications, 10(1), 73-90. https://bit.ly/420qM1W
Neyland, D. (2015). On Organizing Algorithms. Theory, Culture & Society, 32(1), 119-132. https://doi.org/10.1177/0263276414530477
Pavlik, J. V. (2023). Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education. Journalism & Mass Communication Educator, 78(1), 84-93. https://doi.org/10.1177/10776958221149577
Peterson-Salahuddin, C., & Diakopoulos, N. (2020). Negotiated Autonomy: The Role of Social Media Algorithms in Editorial Decision Making. Media and Communication, 8(3), 27-38. https://doi.org/10.17645/mac.v8i3.3001
Rapeli, L. (2024). Illusion of knowledge: is the Dunning-Kruger effect in political sophistication more widespread than before? Journal of Elections, Public Opinion and Parties, 34(3), 573-583. https://doi.org/10.1080/17457289.2023.2214734
Reviglio, U., & Agosti, C. (2020). Thinking Outside the Black-Box: The Case for “Algorithmic Sovereignty” in Social Media. Social Media + Society, 6(2), 2056305120915613. https://doi.org/10.1177/2056305120915613
Ripatti-Torniainen, L., & Mikkola, L. (2023). Journalism Education as Scientific Education: Research University Students’ Engagement with Knowledge. Journalism Studies, 24(2), 270-287. https://doi.org/10.1080/1461670X.2022.2157317
Saldaña, J. (2013). The Coding Manual for Qualitative Researchers (2nd ed.). London: Sage. https://bit.ly/3Pv191P
Schellewald, A. (2022). Theorizing “Stories About Algorithms” as a Mechanism in the Formation and Maintenance of Algorithmic Imaginaries. Social Media + Society, 8(1), 20563051221077025. https://doi.org/10.1177/20563051221077025
Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2), 2053951717738104. https://doi.org/10.1177/2053951717738104
Seaver, N. (2018). What Should an Anthropology of Algorithms Do? Cultural Anthropology, 33(3), 375-385. https://doi.org/10.14506/ca33.3.04
Seo, H., da Silva, M. P., Jona, B., Iqbal, A., Burkett, K. M., Khan, H. U., et al. (2025). Comparative analysis of AI attitudes among JMC students in Brazil and the US: a mixed-methods approach. Online Media and Global Communication. https://doi.org/10.1515/omgc-2024-0060
Shelby, R., Rismani, S., & Rostamzadeh, N. (2024). Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-Reduction. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1-17). Association for Computing Machinery. https://doi.org/10.1145/3613904.3642461
Siles, I., Segura-Castillo, A., Solís, R., & Sancho, M. (2020). Folk theories of algorithmic recommendations on Spotify: Enacting data assemblages in the global South. Big Data & Society, 7(1), 2053951720923377. https://doi.org/10.1177/2053951720923377
Stpiska, A. M., Secler, B., Jurga-Wosik, E. D., & Naro?na, D. (2017). Studenci dziennikarstwa i komunikacji spo?ecznej w Polsce. Motywacje, oczekiwania i plany zawodowe. Studia Medioznawcze, 68(1), 11-24. https://doi.org/10.33077/uw.24511617.ms.2017.68.406
Swart, J. (2021). Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media. Social Media + Society, 7(2), 20563051211008828. https://doi.org/10.1177/20563051211008828
Thurman, N., Dörr, K., & Kunert, J. (2017). When Reporters Get Hands-on with Robo-Writing. Digital Journalism, 5(10), 1240-1259. https://doi.org/10.1080/21670811.2017.1289819
van Dalen, A. (2024). Revisiting the Algorithms Behind the Headlines. How Journalists Respond to Professional Competition of Generative AI. Journalism Practice, 1-18. https://doi.org/10.1080/17512786.2024.2389209
Veenstra, A. S., Wilder, M., Shimu, F., Schlauder, L., & Dousdebés, F. (2024). Journalism Students’ Use, Expectations, and Understanding of Generative AI Tools. Journalism & Mass Communication Educator, 80(1), 115-128. https://doi.org/10.1177/10776958241296474
Wenger, D., Hossain, M. S., & Senseman, J. R. (2025). AI and the Impact on Journalism Education. Journalism & Mass Communication Educator, 80(1), 97-114. https://doi.org/10.1177/10776958241296497
Wilson, C. (2007). The Moral Epistemology of Locke’s Essay. In L. Newman (Ed.), The Cambridge Companion to Locke’s “Essay Concerning Human Understanding” (pp. 381-405). Cambridge: Cambridge University Press. https://doi.org/10.1017/CCOL0521834333.014
Xu, L., Zhang, Y., Yu, F., Ding, X., & Wu, J. (2024). Folk Beliefs of Artificial Intelligence and Robots. International Journal of Social Robotics, 16(3), 429-446. https://doi.org/10.1007/s12369-024-01097-2
Zhu, R., Wang, X., Yu, X., & Chan, P. W. K. (2024). “AI matters, but my STEM sucks”: Determinants of Chinese journalism students’ views on greater AI training in journalism courses. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13230-9
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