Admission requirements
Admitted to the MA Journalism and New Media.
Description
Introducing new technologies inside the newsrooms creates new professional cultures, organizational structures, and business models. Although there has been a recent uptick in the literature that addresses innovation in journalism, the speed with which new technologies develop has left journalists and media scholars playing catch-up. In this course, we will pay attention to different areas of news production, from how journalists gather and analyze data to how they distribute new information using evolving digital platforms and news assistants.
During our sessions, we will draw from theories of innovation in journalism to identify and understand the current challenges journalists face today: Do platforms like Facebook and Google negatively impact newsroom autonomy? How can journalists discern between fake news and essential information in big data? How are algorithmic news recommenders and audience metrics changing how reporters imagine their audiences? Is artificial intelligence a threat to the democratic role of journalism?
Our seminar sessions are tailored to cover new developments in the industry. To achieve this, the sessions draw from a diverse set of articles covering topics such as:
Data journalism
Data analysis and visualization
Platformization and multimodalities
Digital cultures
Open-Source Intelligence
Immersive technologies
News recommenders and voice assistants
Audience metrics
Artificial intelligence
Automation
Course objectives
At the end of this course, the student can recognize the most critical debates around innovations happening today in newsrooms across the world.
The student can demonstrate a critical understanding of contemporary debates about data, journalism, and the relationship between ethics and new technologies.
The student can critically examine innovative technologies in journalism and their use in media projects and formulate an opinion about the opportunities and challenges they bring to the industry.
The students have gained insights into the critical phase of designing, implementing, and evolving new technologies inside newsrooms.
The students can assist with implementing new technologies inside newsrooms to gather, analyze, and disseminate journalistic stories.
The students can reflect on fundamental journalistic questions about technological innovations, such as ethical and legal issues, transparency, fairness, and accuracy.
Timetable
The timetables are available through MyTimetable.
Mode of instruction
Seminar
Part I
The first part of this course includes weeks 1 and 2. During these two weeks, we will have an introductory discussion about theories of innovation in journalism. The sessions will be guided by the course instructor, and we will have guest speakers joining us to discuss recent developments in the newsrooms. Students are expected to do the readings and post appropriate questions online starting in Week 2. During Week 1 we will also form the groups you are going to work with for the rest of the semester.
Part II
The second part of this course runs from Week 3 to Wee 12. Each class will be divided into two hours. A group of students will lead the beginning of each session. In the first part, students will prepare a presentation about the topic of that week and present examples and case studies for the rest of the class. The rest of the class is expected to do the readings and come prepared with discussion points. These could be aspects of the texts that you found exciting or problematic or practical examples you know that we could use as case studies. This will take around an hour, depending on your input. The presenting group will be graded for their class. You can be as creative and innovative as you want.
In the second part of the seminar, the presenting group will invite a relevant guest speaker to class and guide the discussion while the speaker presents their experience in the field. Guest speakers can be academics, journalists, or activists working daily with the technologies that we are studying. In groups, students need to submit -by the previous day- three questions based on the literature and in light of the broader media studies field to ask our guests. Questions will be graded with a short COMPLETE/INCOMPLETE mark.
Part III
Part III includes weeks 13 to 15. During these weeks, we will prepare and present students’ final resolutions (see Assessment section).
Assessment method
Assessment
The assessment for this course consists of three main components.
0 Questions for the guests: In groups, students need to submit three questions via Brightspace the day before our class. These questions will be used to guide the discussion with our guests. Questions will be graded with a short COMPLETE/INCOMPLETE mark. Groups need at least eight COMPLETE marks to move on to the presentations.
1 Students will be evaluated for their in-class moderation and presentation in groups. This includes designing an engaging, coherent, and informative lecture for the rest of their classmates. This grade also includes how the group engaged with the guest speaker.
2 Halfway through the course, we will have a written exam based on the material reviewed during the first weeks, including the assigned articles and the in-class discussions.
3.1 Toward the end of the course, students make a live, in-person, 25-minute group presentation to a panel of journalists and media workers. Each group will be assigned a resolution related to one of the technologies covered during this course. For example, the key for audience metrics technologies could be: “Audience metrics technologies assist in connecting the newsroom with their audiences.” Groups will have 15 minutes to defend their resolution and 10 minutes for questions.
3.2 After those 15 minutes, the panel of experts will take the opposite side of the resolution and ask questions or make statements defending the contrary position. Then, groups will have ten minutes to counterargument those claims and answer the questions. Finally, another group of students will also be assigned to pose counterarguments.
Weighing
In class presentations: 30%
Exam: 40%
Group presentation (Resolution) and questions: 30%
Resit
The resit for the exam will consist of another written, in-person exam based on the assigned readings and lecture content (i.e., same format, more content to cover). The highest possible grade is 7.0.
The resit for the group final presentation and debate will consist of an oral exam, where the group will go through a Q&A session about the presented material. The highest possible grade is 7.0.
Inspection and feedback
How and when an exam review will take place will be disclosed together with the publication of the exam results at the latest. If a student requests a review within 30 days after publication of the exam results, an exam review will have to be organized.
Reading list
Week 1. Data Journalism (8 Feb)
Rogers, S. (2014). Data journalism is the new punk. British journalism review, 25(2), 31-34, DOI: https://doi.org/10.1177/0956474814538181
Casselman, B. (2019, Nov 13). “In Data Journalism, Tech Matters Less Than the People”. The New York Times: https://www.nytimes.com/2019/11/13/technology/personaltech/data-journalism-economics.htm
Jamil, S. (2019): Increasing Accountability Using Data Journalism: Challenges for the Pakistani Journalists, Journalism Practice, DOI: 1080/17512786.2019.1697956
Week 2. Data analysis and visualization (15 Feb)
Horky, T., & Pelka, P. (2017). Data Visualisation in Sports Journalism: Opportunities and challenges of data-driven journalism in German football. Digital Journalism, 5(5), 587-606, DOI: 10.1080/21670811.2016.1254053
Gray, J. (2012, May 31). “What Data Can and Cannot Do”. The Guardian: https://www.theguardian.com/news/datablog/2012/may/31/data-journalism-focused-critical
Dick, M. (2014) Interactive Infographics and News Values, Digital Journalism, 2:4, 490-506, DOI: 10.1080/21670811.2013.841368
Week 3. Platformization and multimodalities (22 Feb)
Meese, J., & Hurcombe, E. (2021). Facebook, news media and platform dependency: The institutional impacts of news distribution on social platforms. New Media & Society, 23(8), 2367-2384, DOI: https://doi.org/10.1177/1461444820926472
Helmond, A. (2015). The platformization of the web: Making web data platform ready. Social media+ society, 1(2), 2056305115603080, DOI: https://doi.org/10.1177/2056305115603080
Week 4. Digital cultures (29 Feb)
Hoffmann, A. L., Proferes, N., & Zimmer, M. (2018). “Making the world more open and connected”: Mark Zuckerberg and the discursive construction of Facebook and its users. New media & society, 20(1), 199-218, DOI: https://doi.org/10.1177/1461444816660784
Gillespie, T. (2014). The relevance of algorithms. Media technologies: Essays on communication, materiality, and society, 167(2014), 167.
Week 5. Open-Source Intelligence (7 Mar)
Dubberley, S, A. Koenig, and D. Murray. 2020. ‘Introduction: The Emergence of Digital Witnesses’. In Digital Witness, 3–11. Oxford University Press. link
Parry, J. 2021. "Open Source Intelligence as Critical Pedagogy; Or, the Humanities Classroom as Digital Human Rights Lab." Interdisciplinary Humanities (2017). link
Week 6. Immersive technologies (14 Mar)
Pavlik, J. (2000) The Impact of Technology on Journalism, Journalism Studies, 1:2, 229-237, DOI: 10.1080/14616700050028226
Jones, S. (2017) Disrupting the narrative: immersive journalism in virtual reality, Journal of Media Practice, 18:2-3, 171-185, DOI: 10.1080/14682753.2017.1374677
Week 7. News recommenders and voice assistants (21 Mar)
Helberger, N. (2019). On the democratic role of news recommenders. Digital Journalism, 7(8), 993–1012. https://doi.org/10.1080/21670811.2019.1623700
Thurman, N., Moeller, J., Helberger, N., & Trilling, D. (2019). My Friends, Editors, Algorithms, and I: Examining audience attitudes to news selection. Digital Journalism, 7(4), 447–469. https://doi.org/10.1080/21670811.2018.1493936
Bastian, M., Helberger, N., & Makhortykh, M. (2021). Safeguarding the Journalistic DNA: Attitudes towards the Role of Professional Values in Algorithmic News Recommender Designs. Digital Journalism, 9(6), 835–863. https://doi.org/10.1080/21670811.2021.1912622
Week 8. Audience metrics (21 Mar)
Helberger, N. (2020) The Political Power of Platforms: How Current Attempts to Regulate Misinformation Amplify Opinion Power, Digital Journalism, 8:6, 842-854, DOI: 10.1080/21670811.2020.1773888
Tandoc Jr, E. C., & Thomas, R. J. (2015). The ethics of web analytics: Implications of using audience metrics in news construction. Digital journalism, 3(2), 243-258. DOI: 10.1080/21670811.2014.909122
Week 9. Exam (28 Mar)
Week 10. Automation (4 Apr)
Lokot, T., & Diakopoulos, N. (2016). News Bots: Automating news and information dissemination on Twitter. Digital Journalism, 4(6), 682-699, DOI: 10.1080/21670811.2015.1081822
Diakopoulos, N., Trielli, D., & Lee, G. (2021). Towards understanding and supporting journalistic practices using semi-automated news discovery tools. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1-30.
Week 11. Field trip - Netherlands Institute for Sound and Vision (11 Apr)
Week 12. Artificial Intelligence (18 Apr)
Broussard, M., Diakopoulos, N., Guzman, A. L., Abebe, R., Dupagne, M., & Chuan, C. H. (2019). Artificial intelligence and journalism. Journalism & Mass Communication Quarterly, 96(3), 673-695, DOI: https://doi.org/10.1177/1077699019859901
Stray, J. (2019). Making artificial intelligence work for investigative journalism. Digital Journalism, 7(8), 1076-1097, DOI: 10.1080/21670811.2019.1630289
Week 13. Group Session (25 Apr)
Week 14. Presentations I (2 May)
Week 15. Presentations II (16 May)
Registration
Enrolment through MyStudymap is mandatory.
General information about course and exam enrolment is available on the website.
Registration Studeren à la carte en Contractonderwijs
Not applicable.
Contact
For substantive questions, contact the lecturer listed in the right information bar.
For questions about enrolment, admission, etc, contact the Education Administration Office: Arsenaal Education Administration Office.
Remarks
Not applicable.