Computational Workshop for MACNM Alumni

 

Live broadcast is available on Douyu: https://www.douyu.com/6226461.

 

Topics:

Date 9:00-12:00 14:00-17:00 Evening
Week 1
Jan 5 Saturday 0. Introduction (Lecturer: Jonathan Zhu) [Notebook]
1. Python Programming I (Lecturer: LAN Ji) [Notebook]
[Video a] [Video b]		 2. Python Programming II (Lecturer: CHEN Zhicong) [Notebook] [Video_a][Video_b][Video_c] Practice
Jan 6 Sunday 3. Web Scraping (Lecturer: GUAN Lu) [Notebook][Video_a][Video_b]  [Video_c][Video_d] 4. Data Visualization (Lecturer: LAN Ji) [Notebook] [Video_a][Video_b]  [Video_c] [Video_d] Practice
Week 2
Jan 12 Saturday 5. Text Mining (Lecturer: GUAN Lu) [Notebook][Video_a][Video_b]  [Video_c][Video_d] 6. User Profiling (Lecturer: CHEN Zhicong) [Notebook][Video_a][Video_b]  [Video_c] Practice
Jan 13 Sunday 7. Network Analysis (Lecturer: Jonathan Zhu) [Notebook][Merged_Video] 8. A/B Test (Lecturer: Jonathan Zhu) [Notebook][Merged_Video] Practice

 

Data and Home Exercise

  • Data: [Link]
  • Home Exercise for Week 1: [Link] [Answer]
  • Home Exercise for Week 2: [Link] [Answer]

The course includes a 2-hour lecture and a 1-hour practice each topic. Web-based tools will be used whenever possible and appropriate to facilitate the distribution of class materials and the interaction among instructors and students.

Venue: M5505, 5/F, Run Run Shaw Creative Media Centre, 18 Tat Hong Avenue, City University of Hong Kong, Kowloon Tong, Hong Kong

 

Preparation:

  • Anaconda Installation (including both Python and Jupyter Notebook)
    1. Anaconda
    2. Make sure Anaconda-Jupyter can run (See an instruction)
  • Apply Twitter Api
    1. Apply Twitter developer App at https://developer.twitter.com/en/apps
    2. Fill the information requirement (user profile, account details, use case details) and finish email verification
  • Python Learning on DataCamp [Link]
    1. Go through the video and exercises (Total 4 hours)
  • Introduction to Computational Methods
    1. How to learn computational communication research (JZ’s slide)
  • Optional Learning Materials 
    1. Interactive learning without installation: w3schools
    2. Video course-Learn Python with Socratica [Youtube] [Bilibili]
    3. Book-Python for Everyone
    4. Jupyter Notebook Documentation and Tutorial

 

Textbook and Readings

Overall  Reading
  1. 张伦，王成军，许小可. (2018). 计算传播学导论. 北京师范大学出版社 (JD.com)
Python Programming I Reading
  1. Python learning on DataCamp and w3schools

Python Programming II

 Reading
  1. Pandas Documentation: https://pandas.pydata.org/pandas-docs/stable/
  2. Pandas Cookbook: https://github.com/jvns/pandas-cookbook
  3. Pandas Lessons: https://bitbucket.org/hrojas/learn-pandas

Web Scraping

 Reading
  1. Liang, H., & Zhu, J. J. H. (2017). Big data, collection of (social media, harvesting). In J. Matthes, C. S. Davis, & R. F. Potter (Eds.), The International Encyclopedia of Communication Research Methods. NJ: Wiley-Blackwell.

Visualization

 Tool
  1. Python package: seaborn

Text Mining

 Reading
  1. 第二章 文本分析简介. In张伦，王成军，许小可. (2018). 计算传播学导论. 北京师范大学出版社

User Profiling

 Reading
  1. 刘鹏. (2015). 计算广告: 互联网商业变现的市场与技术. 人民邮电出版社.
  2. 项亮. (2012). 推荐系统实践. 人民邮电出版社. Link
  3. Tan, P. N., Steinbach, M., & Kumar, V. (2013). Introduction to data mining.

Network Analysis

 Reading
  1. Easley & Kleinberg. (2010). Networks, crowds, and markets
  2. Hanneman & Riddle.  (2005). Introduction to social network methods
Tool
  1. Gephi (https://gephi.org/users/download/)
  2. Python package: NetworkX

A/B Test

  1. Matthew Salganik (2018). Bit by bit: Social research in the digital age. Chapter 4. Running experiments. Available online.