A number of research works across several disciplines rely on social media data. Because of its availability and granularity, studies in political sciences, sociology, journalism, and economics leverage social media data for empirical analysis and experimentation. Beyond social media studies, the methodological approaches used in these studies have been popularized and applied to survey-based research, analysis of political discourse, political competition, and news media, to name a few.
In this course, we will overview social media data analysis, structured along three main axes that have contributed to the understanding of online informational, social, and political ecosystems: social media fragmentation (linked to isolation and so-called echo chamber phenomena), network diversity (linked to news media diets and evaluation of algorithmic recommendations), and polarization (linked to political competition, extremism, and opinion dynamics). Through the analysis of these three types of phenomena, this course provides students with 1) a methodologically structured overview of the landscape of social media studies in scientific literature, and 2) and conceptually robust theoretical understanding of the underlying models that are leveraged in these studies, and that can be applied to a wide range qualitative and quantitative studies in computational social sciences in general.
This course is organized around two types of activity: 1) presentation and collective analysis of different studies leveraging a wide but compactly organized set of models, tools, and theories, and 2) hands-on practical coding experiences and techniques using real-world social media data through the development of group projects. Previous experience with data analysis in Python is required, and the course is conceived so that students with different coding skills to participate and improve their skills. The main goal of the course is for students to boost their ability to leverage new data in research projects.
Requirements: students taking this course should be familiar with loading and treatment of CSV data files on Python using pandas. Basic experience using statistical analysis modules such as scikitlearn is desirable but not required. No previous knowledge of network data is required.
Course dates and hours:
Mon 12 June: 9:00–12:00 & 13:00–16:00
Tues 13 June: 9:00–12:00 & 13:00–16:00
Wed 14 June: 9:00–12:00 & 13:00–16:00
Thu 15 June: 9:00–12:00 & 13:00–16:00
Fri 16 June: 9:00–12:00 & 13:00–16:00
Location: room 34, 27 rue Saint Guillaume
- Pedro RAMACIOTTI MORALES (Sciences Po)
Entry requirements: Intermediate experience coding in Python
Assessment: The evaluation of this course consists of a group project to be developed through the week and presented Friday afternoon. In this small project, student will select datasets available online to propose a political analysis involving either algorithmic recommendation, news media consumption, social networks, online cultural consumption, or some combination of these and other related online phenomena.