PSYC469-23S2 (C) Semester Two 2023

Special Topic

15 points

Start Date: Monday, 17 July 2023
End Date: Sunday, 12 November 2023
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 30 July 2023
  • Without academic penalty (including no fee refund): Sunday, 1 October 2023


Special Topic

Special Topic: Social Media Analytics in Psychology

Posts on social media represent information about the individual posting it. How can we make sense of this reservoir of digital trace data to study social and psychological phenomena? This methods-focused course will provide a foundational overview of quantitative techniques in analysing social media. Students will be equipped with practical skills for simple data mining, social network analysis, machine learning, and natural language processing, with the goal of applying these methods to a research question of their choice. Note that coursework relies heavily on the R programming environment, but prior experience is not required.

Some examples of research questions that we can examine in the course:
1. What can the r/socialanxiety Subreddit tell us about the experiences of individuals with social anxiety?
2. Do popular Twitter accounts use more emotive language?
3. What features of music are associated with stronger Spotify popularity?
4. Are there different streamer communities on Twitch, and what do they talk about?

Learning Outcomes

  • Interact with various Application Programming Interfaces (APIs) to pull data from social/digital media sources (e.g., Reddit, Twitch, Spotify).
  • Construct a simple social network and identify influencers with high centrality.
  • Analyse post sentiment and construct a topic model.
  • Construct a machine learning model to predict post popularity.


Subject to approval of the Head of Department

Contact Person

Kong Meng Liew

For any enquiries, please email


Assessment will be based on a collaborative research paper to be submitted at the end of the semester. Students will work in groups of 2-3 (or individually) to decide on a research question, obtain data, conduct analysis, and write a paper. Students will be graded on the quality of the submitted paper (40%), analysis scripts (30%), peer-evaluation (10%) and class participation (20%).

Textbooks / Resources

Recommended Reading

James, Gareth et al; An introduction to statistical learning : with applications in R ; Springer, 2013.

Szabo, Gabor; Social media data mining and analytics ; John Wiley and Sons, 2019.

There are no required readings for this course. Assigned readings for lectures will be provided online via LEARN.

Indicative Fees

Domestic fee $1,079.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see School of Psychology, Speech and Hearing .

All PSYC469 Occurrences

  • PSYC469-23S1 (C) Semester One 2023 - Not Offered
  • PSYC469-23S2 (C) Semester Two 2023