2025 Social Media Lab – Computational Social Science Summer School – Online [July 14-16, 2025 via Zoom]

Join us for the Social Media Lab’s 2025 Computational Social Science (CSS) Summer School. Explore cutting-edge digital methods in this hands-on workshop series. No coding required — we’ll be using Communalytic, an intuitive platform for analyzing social media discourse.

At this event, you will learn how to:

• Collect publicly accessible social media data for academic research via Communalytic – a no-code research tool developed by the Social Media Lab for studying online discourse; 

• Analyze public discourse from social media data (and non-social media data) using automated text analysis techniques (such as toxicity analysis and topic analysis) and social network analysis.

Attend one session or attend all three! Each introductory session covers a different aspect of computational social science:

• Session 1 – Monday, July 14 (11 AM – 1 PM ET): Intro to Topic Analysis
  Learn how to uncover themes in online conversations using multilingual embeddings and interactive 3D semantic similarity maps.
  
• Session 2 – Tuesday, July 15 (11 AM – 1 PM ET): Intro to Civility Analysis
  Learn how to use Communlaytic Analyzer to access the Perspective API to identify toxic and prosocial comments, as well as assess the overall tone of online discussions.
  
• Session 3 – Wednesday, July 16 (11 AM – 1 PM ET): Introduction to Social Network Analysis
• Discover, visualize and interpret communication and link-sharing networks from conversation data.

Who Should Attend?

The Summer School is open to graduate students, researchers, data librarians, journalists, and analysts interested in CSS, textual, and network analysis of social media data — no prior experience is needed!

Outcomes

By the end of the event, participants will be able to:

• Collect social media data for their own research project and
• Learn how to analyze it to detect anti-social interactions (i.e., harassment, hate speech, extremist content),
• Identify and group together semantically similar social media posts, and
• Identify latent topics in their dataset. 
• In addition, participants will also learn how to generate and visualize various types of networks, including communication and link-sharing networks, which can be used to identify influencers, map shared interests among online actors, study the spread of misinformation and disinformation, and detect signs of possible coordination among seemingly disparate actors.

INSTRUCTORS