Social Media Analytics

Lecturers: Mourad Khayati, Philippe Cudré-Mauroux and Dingqi Yang

Teaching language: English

Level: MSc students

Academic year: Spring 2019

Overview

Structure

Lectures

Exercises


Overview

Description:  The course will cover techniques and algorithms to analyze the structure of large social networks, and to identify their main properties. We start by introducing the basic concepts of social media analytics. Next, the course will delve into studying the main measures and models used for social media networks and techniques applied to identify communities. Then, the course will cover social media application topics, including diffusion/influence in social networks, crowdsourcing on the web, social recommendation and location-based social media.

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


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 taught by Rana Hussein, Natalia Ostapuk, Laura rettig and Akansha Bhardwaj. 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.

 


Lectures

The lectures take place TU 14:15-17:00 in room E230 (UniFR, PER21). The lecture notes for the course will become available as we progress through the semester. Tentative syllabus and slides: