SOCIAL COMPUTING

Academic Year 2023-2024

Teachers

Stefano Mizzaro
David La Barbera

Course Year

Unit Credits

Teaching Period

First Period

Course Type

Characterizing

Prerequisites. Basic notions of mathematics, Web programming, and algorithms and data structures will be taken for granted.

Teaching Methods. Normal lectures, flipped classroom, laboratory activities.

Verification of Learning. Written plus oral exam. Homework assignments and midterm project assignments will also be evaluated. Alternative programs for Erasmus students are possible in principle and have to be discussed with the instructor.

The criteria for rating decision are those decided by the “Corso di Studi” and can be found at: https://www.uniud.it/it/didattica/corsi/area-scientifica/scienze-matematiche-informatiche-multimediali-fisiche/laurea/internet-of-things-big-data-machine-learning/corso/regolamento-corso/all-B2

More Information. Teaching material (slides, etc.) will be provided by means of the moodle and teams e-learning platforms during the course.

Objectives

https://www.uniud.it/it/didattica/info-didattiche/regolamento-didattico-del-corso/l-internet-things-big-data-machine-learning/all-B2

The aim of the course is to provide to the student the foundational knowledge and the practical skills concerning the area of social informatics. The course will discuss both the so-called Social Media (Facebook, Twitter, etc.) and the Crowdsourcing phenomenon. In the first case, social behavior is supported by computational systems; in the second case, computational systems are supported by social behavior. The course will deal with conceptual topics, theoretical foundations, and practical applications. The course is divided into the following four parts:

1.Introduction

a.Examples of social media, relevance to data science (socials are a source of data and users, and a ground where interesting phenomena happen)

b.Examples of Crowdsourcing. Success stories and failures.

2.Social media

a.Concepts: Definition. Examples. Classification (generalist, verticals, private) b.Foundations: Historical background (social network analysis, network science). Elementary network science.

c.Applications. APIs to access data from socials (case studies: Twitter or Facebook or Telegram)

3.Crowdsourcing

a.Concepts: Definitions, Examples. Collective intelligence

b.Foundations: The general case of Human computation. Characteristics needed for successful Crowdsourcing. Computability (brief account)

c.Applications: usage of a Crowdsourcing platform (Amazon’s Mechanical Turk) to design and run experiments. Analysis of results.

4.Case studies and specific issues

a.Ethical, moral, legal aspects

b.Economic aspects (SEO, business models, crowdfunding)

c.Social-aware programming (multiagent systems, society design; genetic algorithms; map/reduce)

d.Hybrid systems

Texts

For each lecture, specific textbooks and other study material will be defined.

Academic Year 2023-2024