Computational Social Media

The course will present a human-centered view of computational social media. It uses a multidisciplinary approach and integrates concepts from media studies, multimedia information systems, machine learning, and network science to present the socio-technical fundamentals needed to understand user motivations and behavior, and analyze content in platforms like Twitter/X, Instagram, YouTube, and TikTok. Students will learn approaches for classification, discovery, and interpretation of phenomena in social media.

The content is organized around trends in social media, introducing concepts and models of general applicability.

1. Introduction. A brief history of social media. Networked individualism.

2. Friending. A human-centered review of social network research. Users, communities, and networks. Privacy and the real-name web.

3. Tweeting. From random chatter to worldwide pulse. Followers, hashtags, events, and network effects. Analyzing real-life phenomena on information networks. Misinformation in social media.

4. Shooting. Photo sharing and tagging. Media, user and community analysis enabled by photo sharing. Ephemeral social media.

5. Moving. Location-based social networks. Individual and network phenomena revealed by mobility data. Urban computing.

6. Watching. Social video as a media phenomenon. Multimodality in social video. Attention and social video.

7. Crowdsourcing. Crowdsourced tasks and crowdworkers. Uses of crowdsourcing in social media research. Crowdsourcing and social participation. Content moderation.

8. The Future. Social media and AI from a global perspective. Effects of social media on society and the environment. Fairness, Accountability, Transparency, and Ethics in social media.