2024 Social Media Lab – Computational Social Science Summer School 

Join us for the Social Media Lab’s 2024Computational Social Science Summer School, sponsored by the Canadian Digital Media Research Network (CDMRN). The Summer School will bring together graduate students, postdoctoral researchers, and early career researchers interested in computational social science. 

Who can attend?

This intensive invite-only training event is open to members of the CDMRN network (PI/Co-PI/HQPs). There is no cost for CDMRN members to attend. Travel, dormitory accommodation in a private room, and meals will be covered by the CDMRN and the Social Media Lab. At this event, participants will have an opportunity to network with peers and 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 at Toronto Metropolitan University for studying online discourse; 

• Analyze social media data using automated text analysis techniques (such as toxicity analysis and topic modelling) and social network analysis.

By the end of the event, participants will be able to collect social media for their own research project and learn how to analyze their data to detect anti-social interactions (i.e., harassment, hate speech, extremist content, etc.), identify and group together social media posts that are semantically similar 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 mis/dis-information and detect signs of possible coordination among seemingly disparate actors.

REGISTRATION

If you are a member of CDMRN and would like to attend, check your inbox and look for the link to register in the latest edition of the CDMRN Newsletter – March 2024 or contact Philip Mai [email protected]

INSTRUCTORS