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Social Media Data Analytics | Spring 2019 Syllabus

Course Description:

This class will help you develop data mining and social media analysis skills using Python 3. It will also ask you to think critically about the ethical use of social media data. This is a hands-on, interdisciplinary data analytics class for those who have at least a casual familiarity with Python. If you haven’t taken a course on Python, you will want to complete “Learn Python the Hard Way” tutorials ($30) or a Codecademy course ($20-$40) on Python 3 on your own before this class.  As a Digital Humanities course, it considers how social media has been used to support social justice and political change movements, the ways in which social media data is currently used by corporate entities, and engages in the discussion of ethical data usage. The course is organized around three projects that involve data collection, analysis, and presentation using the techniques learned in the class.

Learning Outcomes:

This course will guide you in developing fundamental digital research skills, including how to:

  • Use different methods for collecting, analyzing, and exploring social media data for research and development purposes.
  • Utilize various Application Programming Interface (API) services to collect data from different social media sources such as Twitter, Facebook, and more.
  • Process the collected data – primarily structured – using methods involving correlation, regression, and classification to derive insights about the sources and people who generated that data. 
  • Analyze unstructured data – primarily textual comments – for themes and trends. 

Required Texts:

Matthew A. Russell & Mkihail Klassen, Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More, 3rd Edition (O’Reilly, 2019).

DatePre-Class Actvity 1Pre-Class Activity 2In Class ActivitiesAssignments
4/2/2019
  1. Course, instructor, & classmate introductions
  2. Slack orientation & activities
  3. Work personality survey & reflection
4/4/2019Read Mining the Social Web (MTSW), Preface, Appendices A-C. Follow any suggestions that will help you become more familiar with the technical aspects of our work.
  1. Update your operating system
  2. Complete command line tutorial
  1. Answer homework questions
  2. Set up Docker environment
4/9/2019Read:
  1. Mining the Social Web 1.1-1.3.2
  2. Restricted Uses of the Twitter API
Read: Fiesler, Casey, and Nicholas Proferes. “‘Participant’ Perceptions of Twitter Research Ethics.” Social Media + Society, (January 2018).
  1. Answer homework questions
  2. Discuss Feisler article & twitter research ethics
  3. MTSW Exploring Twitter data with the API: 1.3.3 & 1.3.4
4/11/2019Run code from MTSW: 1.3.3 & 1.3.4 and add comments to explain what the code is doing. Read: MTSW: 1.4.0.
  1. Quiz: Ethical Twitter Research
  2. Answer homework questions
  3. Explore Twitter Archiving with Google Sheets
4/16/2019Listen (16min): Sheilah Kast & Jonna McKone, “The Politics of Archiving #BlackLivesMatter,” Maryland Morning with Sheilah Kast (15 May 2015). Read: Whitelaw Reid, “Black Twitter 101: What is it? Where did it originate? Where is it headed?” UVa Today (28 November 2018).
  1. Discuss alternative learning paths for students
  2. Explore scraped Tweets & pose research questions
  3. Project pitches & choose teams
  4. Discuss teammate working styles & communication strategies
  5. Discuss project product possibilities (infographic or data dashboard with white paper OR website)
Project pitches due
4/18/2019Read: MTSW: 1.4.1 - 1.4.5Read: Ed Summers, “Introducing Documenting the Now,” Maryland Institute for Technology in the Humanities (16 February 2016).
  1. Draft specific questions on the Twitter data
  2. Tutorial: How to work with Twitter data
  3. Set project milestones based on research questions, team members’ strengths/skills, and deliverables.
Twitter data for project
4/23/2019Watch: Antony Funnel, “Meet the Digital Librarians Saving Social Media Posts to Protect Human Rights,” Future Tense (29 August 2017).Read:
  1. Bergis Jules, “Some Thoughts on Ethics and DocNow,” News.DocNow (3 June 2016).
  2. Nora Caplan-Brickler, “Preservation Acts: Toward an Ethical Archive of the Web,” Harper’s (December 2018).
  1. Warm-up
  2. Tutorial: How to split Twitter data into separate text files
  3. Workshop on Open Refine
  4. Continue Project 1
4/25/2019Read: Ahmed Al-Rawi, “Assessing Public Sentiments and News Preferences on Al Jazeera and Al Arabiya,” (2017).Sentiment Analysis Lesson in Jupyter notebook
4/30/2019Read: Ahmed Al-Rawi, “Assessing Public Sentiments and News Preferences on Al Jazeera and Al Arabiya,” (2017).Read: Bruns, “The Arab Spring and Social Media: English and Arabic Twitter Users and Their Networks” (2013).
  1. Answer questions
  2. Discuss sentiment analysis results and how to visualize them.
  3. Critically analyze polarity as sentiment analysis method
5/2/2019Continue working on project 1.Continue working on project 1. Troubleshoot challenges.
5/7/2019Read: Bruns, “The Arab Spring and Social Media: English and Arabic Twitter Users and Their Networks” (2013).Continue working on project 1. Troubleshoot challenges.
5/9/2109Continue working on project 1Continue working on project 1. Troubleshoot challenges.Project 1 – Twitter
5/14/2019Team Project Lightning TalksProject 1 Team Lightning Talk
5/16/2019Read: Ahmed Al-Rawi, “Framing the Online Women’s Movements in the Arab World,” (2014).Qualitative data analysis tutorial with Voyant Tools & Python
5/21/2019Read: MTSW 5.4 “Querying Human Language Data with TF-IDF”. Read this entire section, including the code.
  1. Text analysis tutorial: TF-IDF with Voyant & NLTK
  2. Brainstorm topics, sources, and questions for final project: web-scraping & qualitative data analysis
5/23/2019Read: MTSW: 5.4.4, 5.4.5, 5.5
  1. Webscraping tutorial
  2. Review research process
  3. Review text analysis methods
  4. Decide on project topics & teams
  5. Teammate introductions and goalsetting
Project 2 Pitches Due
5/28/2019Read: MTSW: chapter 6Read: Hinds, “What Demographic Attributes Do Our Digital Footprints Reveal? A Systematic Review” (2018).
  1. Warm-up: discuss work personalities & work plan with teammates
  2. Ensure students have their sources or a plan to get them by the end of class.
  3. Crowdsourcing webscraping strategies: students share how they have scraped sources
  4. Wrap-up: Finalize project topics and ensure students either have their sources or a plan to scrape them by the end of class.
5/30/2019Work on Project 2. Troubleshoot challenges
6/4/2019Project 3: Lightning Talks - Share findings, visualizations, challenges & peer feedback. Strengths, suggestions.
6/6/2019
  1. Project work time
  2. Class wrap up:
    • What worked or didn’t work in helping you learn?
    • What would help next time?
    • How has your perspective changed since the beginning of the class?
    • What will you take away from the course?
    • How did the format of the class affect your learning and your motivation?
6/13/2019Project 2: Web scraping & qualitative data analysis
Individual Reflection

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