Computational Social Media

DH-500 / 4 credits

Teacher: Gatica-Perez Daniel

Language: English

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, Instagram, YouTube, and TikTok. Students will learn computational methods to understand phenomena in social media.

Content

 

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, Instagram, YouTube, and TikTok. Students will become familiar with computational approaches for classification, discovery, and interpretation of phenomena in social media.

The content is organized around trends in social media, introducing computational 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. Multimedia techniques (audio,video,text) to analyze social video.

7. Crowdsourcing. Models to analyze crowdsourced tasks and workers. Uses of crowdsourcing in social media research. Crowdsourcing and social participation.

8. Society. Social media from a global perspective. Effects of social media on society. Fairness, Accountability, Transparency, and Ethics in social media.

 

Keywords

Social Media, Social Networks, Multimedia, Machine Learning.

Learning Prerequisites

Required courses

Students from other disciplines can talk to the instructor during the first lecture of the course.

Recommended courses

Applied Data Analysis

Machine Learning for Digital Humanities

 

 

Learning Outcomes

By the end of the course, the student must be able to:

  • Apply socio-technical fundamentals to understand motivations, characterize behavior, and analyze content of social media users and communities

Transversal skills

  • Plan and carry out activities in a way which makes optimal use of available time and other resources.
  • Assess progress against the plan, and adapt the plan as appropriate.
  • Evaluate one's own performance in the team, receive and respond appropriately to feedback.
  • Take account of the social and human dimensions of the engineering profession.
  • Manage priorities.
  • Give feedback (critique) in an appropriate fashion.
  • Respect relevant legal guidelines and ethical codes for the profession.
  • Make an oral presentation.
  • Summarize an article or a technical report.
  • Demonstrate a capacity for creativity.
  • Demonstrate the capacity for critical thinking
  • Write a scientific or technical report.

Teaching methods

Lectures

Paper presentations

Group discussions

Project design, development, and implementation 

 

Expected student activities

Homeworks

Paper presentations

Group discussions

Group project

Assessment methods

Multiple methods during the semester: homeworks; paper presentation and group discussion, and group project.

Supervision

Office hours Yes
Assistants Yes
Forum No

Resources

Websites

Moodle Link

In the programs

  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational Social Media
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 1 Hour(s) per week x 14 weeks
  • Type: mandatory
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational Social Media
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 1 Hour(s) per week x 14 weeks
  • Type: mandatory
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational Social Media
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational Social Media
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Exam form: During the semester (summer session)
  • Subject examined: Computational Social Media
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Exam form: During the semester (summer session)
  • Subject examined: Computational Social Media
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Exam form: During the semester (summer session)
  • Subject examined: Computational Social Media
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 1 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Computational Social Media
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 1 Hour(s) per week x 14 weeks
  • Type: optional

Reference week

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