PSYC469-24S1 (C) Semester One 2024

Special Topic

15 points

Details:
Start Date: Monday, 19 February 2024
End Date: Sunday, 23 June 2024
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 3 March 2024
  • Without academic penalty (including no fee refund): Sunday, 12 May 2024

Description

Special Topic

PSYC469 Special Topic - Social Media Analytics in Psychology

Psychological researchers are increasingly turning to new methods of research beyond traditional survey, experimental, and psychophysiological methodology. One emerging method has been utilizing social media data as a naturalistic resource for individuals’ perspectives, attitudes, and behaviours, through computational methods. This course will introduce students to these computational techniques to enable students to use these novel methods for their own research. Accordingly, this course will break down methods used to obtain social media and other digital trace data, beginning with data scraping using Application Programme Interfaces (APIs), and analysis techniques like network analyses, and simple natural language processing (e.g., sentiment analysis and topic modelling). Examples will be given on how these analyses can be applied to a variety of psychology subfields, such as I/O Psychology, Forensic Psychology, and Clinical and Health Psychology.

Learning Outcomes

  • By the end of the course, students should gain the practical skills needed to conduct simple quantitative analyses of social media data. They should be able to:

  • 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.

  • Become comfortable with working in the R environment.

Prerequisites

Subject to approval of the Head of Department

Timetable 2024

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 11:00 - 13:00 Jack Erskine 242
4 Mar - 31 Mar
22 Apr - 2 Jun
Lecture B
Activity Day Time Location Weeks
01 Tuesday 15:00 - 17:00 Jack Erskine 241
19 Feb - 3 Mar

Lecturer

Kong Meng Liew

Assessment

Assessment Due Date Percentage  Description
Research poster and recorded presentation 40% Research poster and recorded presentation
Commented analysis scripts 30% Commented analysis scripts
Weekly reactions 20% Weekly reactions
Lab assignments 10% Lab assignments

Textbooks / Resources

Required Texts

James, G., Witten, D., Hastie, T., & Tibshirani, R; An introduction to statistical learning ; New York: Springer, 2013.

Recommended Reading

Szabo, G., Polatkan, G., Boykin, P. O., & Chalkiopoulos, A; Social media data mining and analytics ; John Wiley & Sons, 2018.

Indicative Fees

Domestic fee $1,110.00

* 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

  • PSYC469-24S1 (C) Semester One 2024