APSK15774U Elective course - Social Technologies

Volume 2026/2027
Education

The course is open to:

  • Bachelor Programme in Psychology

 

Full-degree students enrolled at the Faculty of Social Science, UCPH 

  • Bachelor and Master Programmes in Psychology
  • Master Programme in Social Data Science
  • Master programme in Political Science

 

The course is open to:

  • Exchange and Guest students from abroad

Curriculum - UCPH

Content

The way people connect, communicate, and consume information is increasingly influenced by social technologies like social media platforms, recommender systems, and large language models. In this course, we explore social technologies and their implications by considering:

  • how human behavior is shaped by technology,
  • how technology is shaped by human behavior,
  • emergent outcomes of human interactions with and through technology, and
  • the challenges of designing and regulating social technologies. 

 

Class sessions will include lectures and activities on topics such as online communities, collective intelligence, content moderation, recommender systems, large language models, platform power, and tech policy. Through lectures, students will learn about how social technologies work from a technical perspective, and how they steer and adapt to social dynamics from a social scientific perspective. Through activities, students will practice analyzing and anticipating ramifications of social technologies. In addition, students will be expected to participate in between-class activities in which they experiment with their own use of social technologies (e.g., by deactivating apps for periods of time and participating in online communities).

The course reading list is diverse and interdisciplinary. In order to gain a holistic understanding of the relationship between technology and social behavior, students will be challenged to engage with methods and concepts beyond their main program of study.

To be eligible for the exam, students are required to engage in the in-class and between-class activities and submit a project proposal to be approved by the instructor.

Learning Outcome

By the end of the module, students will be able to:

  • describe and account for relevant concepts and themes covered by the elective course
  • describe and account for relevant methodological approaches in relation to the subject matter for the elective course
  • explain contexts, analyse and conduct procedures relevant to the elective course under supervision
  • discuss themes/problems relevant to the elective course or interpret cases/data related to the elective course.

Main Literature:

  • Simon (1969). The sciences of the artificial, ch 1, understanding the natural and artificial worlds. MIT Press (3rd ed.). 21 standard pages.
  • Kraut et al. (1998). Internet paradox: A social technology that reduces social involvement and psychological well-being?. American Psychologist. 29 standard pages.
  • Rahwan et al. (2019). Machine behaviour. Nature. 5 standard pages.
  • Hollan & Stornetta (1992). Beyond being there. ACM CHI. 17 standard pages.
  • Marwick & boyd (2010). I tweet honestly, I tweetpassionately: Twitter users,context collapse, and theimagined audienc. New Media & Society. 25 standard pages.
  • Zhang et al. (2024). Form-from: a design space of social media systems. CSCW. 67 standard pages.
  • Anderson (1972). More is different. Science. 10 standard pages.
  • Howe (2006). The rise of crowdsourcing. Wired Magazine. 11 standard pages.
  • Hong & Page (2004). Groups of diverse problem solvers can outperform groups of high-ability problem solvers. PNAS. 14 standard pages.
  • Klonick (2018). The new governors: the people, rules, and processes governing online speech. Harvard Law Review. 98 standard pages.
  • Myers West (2018). Censored, suspended, shadowbanned: user interpretations of content moderation on social media platforms. New Media & Society. 24 standard pages.
  • Shardanand & Maes (1995). Social information filtering: algorithms for automating “word of mouth”. ACM CHI. 15 standard pages.
  • Tufekci (2018). Youtube, the great radicalizer. New York Times. 3 standard pages.
  • Narayanan (2023). Understanding social media recommendation algorithms. Knight First Amendment Institute. 29 standard pages.
  • Bender et al. (2021). On the dangers of stochastic parrots: Can language models be too big? 🦜. FAccT. 28 standard pages.
  • Farrell et al. (2025). Large AI models are cultural and social technologies. Science. 12 standard pages.
  • Burton et al. (2024). How large language models can reshape collective intelligence. Nature Human Behavior. 37 standard pages.
  • Zuboff (2015). Big other: surveillance capitalism and the prospects of an information civilization. Journal of Information Technology. 34 standard pages.
  • Tufekci (2014). Engineering the public: big data, surveillance and computational politics. First Monday. 33 standard pages.
  • Stray et al. (2020). What are you optimizing for? Aligning recommender systems with human values. ICML. 14 standard pages.
  • Ovadya & Thorburn (2023). Bridging systems. Knight First Amendment Institute. 37 standard pages.
  • Fukuyama (2021). Making the internet safe for democracy. Journal of Democracy. 10 standard pages.

Orben & Matias (2025). Fixing the science of digital technology harms. Science. 11 standard pages.

Syllabus cf. curriculum: The syllabus can be compulsory or combined compulsory/​self-selected.
Elective (BA) (7.5 ECTS): approx. 600 standard pages
Elective (MA) (7.5 ECTS credits): approx. 800 standard pages

An up-to-date syllabus list will be available in the course room on Absalon just before the start of the semester.

If the course has previously been offered, syllabus lists can be found here: Absalon

Students should be willing to engage with theories and concepts from both the social and computational sciences.
Each class session is three hours and divided into three blocks. In each class, there will be at least one lecture block and one activity block.
  • Category
  • Hours
  • Class Instruction
  • 36
  • Total
  • 36
Written
Oral
Individual
Collective
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
Credit
7.5 ECTS
Type of assessment
Home assignment
Type of assessment details
Students will be assessed on a written home assignment. Each individual student will be tasked with writing a paper (BA: max. 8 pages; MA: max. 12 pages) in which they critique a specific social technology of their own choosing (e.g., ChatGPT, Perplexity, Reddit, TikTok, Amazon MTurk, Metaculus, arXiv, Absalon, or some other relevant example). In the paper, the student is expected to demonstrate knowledge of the course content by applying it to:
- clearly describe what kinds of user behaviors the technology enables, encourages, or discourages,
- explain which psychological processes or mechanisms the technology engages,
- discuss both observed and potential unintended consequences of the technology’s development and adoption, and
- suggest concrete design and/or policy improvements that could mitigate problems, enhance benefits, or better align it with desired social outcomes.

To be eligible for the exam, students must submit a project proposal to be approved by the instructor in which they identify the social technology they will critique.
Examination prerequisites

Students must actively engage in the weekly class activities and submit a project proposal for approval to be eligible for the exam.

For all elective subjects, a minimum of 75% of alle classes must be attended as a prerequisite for submitting the final exam. However, the teaching is based on full participation.

Aid
All aids allowed

Regarding the use of generative AI:  Bachelor i Psykologi - KUnet

Marking scale
7-point grading scale
Censorship form
No external censorship
Exam period

Exam information:

The examination date can be found in the exam schedule    here

The exact time and place will be available in Digital Exam from the middle of the semester. 

Re-exam

Reexam information:

The reexamination date/period can be found in the reexam schedule    here

Criteria for exam assesment

Students are assessed on the extent to which they master the learning outcome for the course.

 

To obtain the top grade “12”, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.

 

To obtain the passing grade “02”, the student must in a satisfactory way be able to demonstrate a minimal acceptable level of the knowledge, skills and competencies listed in the learning outcomes.