COMM6320: Digital Research

COMM6320 (2024-2025 Term 2)
Teacher: Prof. LIANG Hai (NAH310)
NAH209: Wed. 10:30 AM - 1:15 PM

Textbooks:

Research Design (Weeks 1-3):

• Salganik, M. J. (2017). Bit by bit: Social research in the digital age.

Web Data Collection (Weeks 4-5):

• Munzert, S., Rubba, C., Meißner, P, & Nyhuis, D. (2015). Automated data collection with R.

Text as Data (Weeks 6-9):

• Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data.
• Rothman, D. (2024). Transformers for Natural Language Processing. (3rd Edition).

Network Analysis (Weeks 10-13):

• Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks.
• Kolaczyk, E. D. & Csardi, G. (2014). Statistical analysis of network data with R.

Image as Data (Week 14):

• Williams, N. W., Casas, A., & Wilkerson, J. D. (2020). Images as data for social science research.

Reference Books:

• Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics.
• Kabacoff, R. I. (2011). R in action: Data analysis and graphics with R.
• Kumar, A. & Paul, A. (2016). Mastering text mining with R.
• Silge, J. & Robinson, D. (2017). Text mining with R: A Tidy approach.
• Wickham, W. (2010). ggplot2: Elegant Graphics for Data Analysis (Use R!).
• Hadley Wickham

Course Materials:

• Syllabus
• Reading List by Week
• Prerequisites: R Programming + Statistics
• Schedule with Slides
  • 08/01 Week 1: Introduction
    • CSS
    • Self-learning: R Programming + Videos
  • 15/01 Week 2: Design I
    • Big data, online survey
    • Self-learning: Wired statistics I-III
  • 22/01 Week 3: Design II
    • Digital field experiment, mass collaboration
    • Self-learning: Wired statistics IV-V
  • 05/02 Week 4: Data Collection I (API)
    • API
    • Lab: Rcurl, httr, rvest
  • 12/02 Week 5: Data Collection II (Scraping) + Coding Crawling
    • Web scraping
    • Lab: Rcurl, httr, rvest
  • 19/02 Week 6: Text Mining I
    • BoW, Vector-space models
    • Lab: quanteda
  • 26/02 Week 7: Text Mining II
    • Clustering, topic modeling, machine learning
    • Lab: quanteda.textmodels, stm
  • 05/03 Week 8: Text Mining III
    • Word embedding, deep learning
    • Lab: word2vec, h2o, keras
  • 12/03 Week 9: Text Mining IV + Coding Texts
    • Transformers, LLMs
    • Lab: text, grafzahl, rollama, llamafile
  • 19/03 Week 10: Social Network Analysis I
    • Nodes, edges, centralities
    • Lab: sna, igraph
  • 26/03 Week 11: Social Network Analysis II
    • Network cohesion, communities
    • Lab: sna, igraph
  • 02/04 Week 12: Social Network Analysis III
    • Ego network analysis
    • Lab: egor
  • 09/04 Week 13: Social Network Analysis IV + Coding Networks
    • Network formation models
    • Lab: sna, igraph
  • 16/04 Week 14: Multimedia Analysis
    • Computer vision, audio/image analysis
    • Lab: googleCloudVisionR, keras