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 sites like Facebook, Twitter and YouTube. Students will learn computational methods to infer individual and networked phenomena in social media.

Content

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.
  • 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 discussion, and group project.

Supervision

Office hours Yes
Assistants Yes
Forum No

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
  • 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
  • Exam form: During the semester (session free)
  • Subject examined: Computational Social Media
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 1 Hour(s) per week x 14 weeks
  • Exam form: During the semester (session free)
  • Subject examined: Computational Social Media
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 1 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22