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Course

DH-500: Computational Social Media

Summary

The course integrates concepts from media studies, machine learning, multimedia, and network science to characterize social practices and analyze content in platforms like Twitter/X, Instagram, YouTube, and TikTok. Students will learn computational methods to understand phenomena in social media.

Lectures

Instructor

Daniel Gatica-Perez

Official source

https://edu.epfl.ch/coursebook/en/computational-social-media-DH-500

Moodle Page

http://go.epfl.ch/DH-500

About this result

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Summary

Lectures

Instructor

Daniel Gatica-Perez

Official source

https://edu.epfl.ch/coursebook/en/computational-social-media-DH-500

Moodle Page

http://go.epfl.ch/DH-500

About this result

Ontological neighbourhood

Computer engineering

Social networks: Social media

Lectures in this course (22)

Twitter API Basics: Data Access Log in to Mediaspace to watch this video

Covers the basics of the Twitter API, including rate limits and data access.

Understanding Social Media Log in to Mediaspace to watch this video

Covers the goals, definition, types, and computational analysis of social media, emphasizing the study of human behavior through digital traces.

Twitter: A Human-Centric View Log in to Mediaspace to watch this video

Explores the human-centric view of Twitter, covering user interactions, geolocation, hashtags, and real names.

Twitter Mood Analysis Log in to Mediaspace to watch this video

Explores the analysis of mood expressed on Twitter using longitudinal data and text analysis tools, emphasizing the importance of considering biased data.

Social Image Systems: Past, Present, and Research Insights Log in to Mediaspace to watch this video

Explores social image systems, from past practices to present platforms and research insights, including tagging motivations and participatory culture.

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