Social Media Analytics

Lecturers: Mourad Khayati and Philippe Cudré-Mauroux

Teaching language: English

Level: MSc students

Academic year: Spring 2024

Overview

Description: The course will cover techniques and algorithms to analyze the structure of social networks and identify their main properties. We start by studying the central concepts of social media analytics (e.g., data structures, similarity/centrality measures, traversal algorithms, etc.). Next, the course will delve into studying various algorithms to identify communities in social networks. Then, the course will cover advanced social network applications, including diffusion/influence, crowdsourcing, social recommendation, social mining, and link analysis.

Learning outcomes: On successful completion of this course, you will be able to:

Collect and store social media networks and identify their main properties
Apply clustering techniques to detect social communities
Optimize information diffusion on social networks
Boost recommendation systems using social networks
Apply matrix factorization/decomposition techniques to extract latent information

Structure

Teaching format: This course consists of lectures and exercises/labs. The weekly/bi-weekly exercises are an important part of the course.

Textbook: The textbook for the course is Social Data Mining: an Introduction, First edition, Cambridge University Press, Reza Zafarani, Mohammad Ali Abbasi and Huan Liu, 2014

Exercises: The exercises will be given by Manuel Mondal. It is highly recommended to solve the exercises before attending the exercise session. You need to pass half of the exercises to be admitted for the final exam. Solving the exercises will be the best way to prepare for the final exam.

Syllabus

The lectures take place TU 14:15-17:00 in room F130 (UniFR, PER21). The lecture notes for the course will become available as we progress through the semester. Tentative syllabus and slides:

Lect 01: Social Media Analytics Basics
Lect 02 (Chap. 2 in the textbook): Network Representation and Traversal
Lect 03 (Chap. 3 in the textbook): Network Measures
Lect 04 (Chap. 6 in the textbook): Community Structure Analysis
Lect 05 (Chap. 4 in the textbook): Network Models
Lect 06 (Chap. 7 and Chap. 8 in the textbook): Diffusion, Assortativity, and Influence
Lect 07 (Chap. 9 in the textbook): Social Recommendation
Lect 08 (Chap. 5 in the textbook): Social Graph Mining
Lect 09: Link Analysis
Lect 10: Crowdsourcing
Lect 11: Summary