OBME 2130 - Social Media Analysis

This course is offered to students interested in social media's role in an increasingly connected media landscape. This class will investigate how social media platforms such as YouTube, reddit and others offer a vantage point to monitor social and political dynamics around specific public issues. The course will introduce technical and conceptual methods to collect, analyze and visualize data from social media platforms (Youtube comments, online forums, etc.). We will focus on methods related to “computational social sciences” - mostly text and network analyses. The course also offers the opportunity to work on an original research collective project using quantitative text analysis. Students will be invited to produce an original empirical analysis of a social media corpus, formulate a pertinent research question, and design an adequate empirical protocol to answer this question. As such, this class is also a research design class. Theoretical discussions about recent scientific papers (case studies or methodological articles) will alternate with more practical tutorials such as: building a corpus using existing social media APIs, practicing various data sciences algorithms (topic modeling, sentiment analysis, word embedding, supervised learning, complex network spatialization etc.) we will learn to use thanks to AI powered coding assistants.
Learning Outcomes
1. text analysis
2. social network analysis
2. research design
3. computational social sciences
4. data visualization
Professional Skills
Social network data collection
Social network data analysis
LLMs
Website production (CMS)

Jean-Philippe COINTET,Camille CHANIAL

English

- In Class Presence: 2 hours a week / 24 hours a semester
- Online learning activities: 20 minutes a week / 4 hours a semester
- Reading and Preparation for Class: 45 minutes a week / 18 hours a semester
- Research and Preparation for Group Work: 2 hours a week / 24 hours a semester
- Research and Writing for Individual Assessments: 20 minutes a week / 4 hours a semester

Prior skills in coding are not required; however, students willing to take this class are strongly encouraged to practice the basics of coding in Python, such as defining a function, creating loops, and using the “pandas” library.

Spring 2024-2025

There will be two main assessments during the semester. The most important one (2/3 of the grade) is a collective project that will be presented during the last session of the semester. The quality of the web delivery of the report, only due at a later stage in the semester, will also be integrated into the final grade. An individual take-home paper will also be graded around the middle of the semester. Active participation during the class discussions can also be rewarded with an extra point

The pedagogical format is strongly oriented toward a workshop-style class. Typically, a short theoretical introduction will first be given. A discussion of the reading will follow before the class turns into an applied mode, wherein students will practice data analysis by themselves. It is required to bring your laptop to class.