Fiches de cours

Computational Social Media

DH-500

Lecturer(s) :

Gatica-Perez Daniel

Language:

English

Withdrawal

It is not allowed to withdraw from this subject after the registration deadline.

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

 

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 the motivations, characterize the behavior, and analyze the content and relations of social media users and communities in sites like Twitter, Facebook, and YouTube. 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 Facebook research. Users, communities, and networks. The real-name web.

3. Tweeting. From random chatter to worldwide pulse. Followers, hashtags, events, and network effects. Analyzing real-life phenomena on Twitter. 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. YouTube 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. Privacy and social media. Effects of social media on society.

 

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:

Transversal skills

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

Resources

Websites

In the programs

    • Semester
       Spring
    • Exam form
       During the semester
    • Credits
      4
    • Subject examined
      Computational Social Media
    • Number of places
      30
    • 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
    • Credits
      4
    • Subject examined
      Computational Social Media
    • Number of places
      30
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Practical work
      1 Hour(s) per week x 14 weeks
    • Semester
    • Exam form
       During the semester
    • Credits
      4
    • Subject examined
      Computational Social Media
    • Number of places
      30
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Practical work
      1 Hour(s) per week x 14 weeks
    • Semester
    • Exam form
       During the semester
    • Credits
      4
    • Subject examined
      Computational Social Media
    • Number of places
      30
    • 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     
Under construction
 
      Lecture
      Exercise, TP
      Project, other

legend

  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German