Social Media Analytics
Lecturers: Mourad Khayati, Philippe Cudré-Mauroux and Dingqi Yang
Teaching language: English
Level: MSc students
Academic year: Spring 2017
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:
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 of 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 Paolo Rosso, Laura rettig, Alisa Smirnova, Artem Lutov and Rana Hussein. It is highly recommended, though not mandatory, to solve the exercises before attending the exercise session. Solving the exercises will be the best way to prepare for the mid-term exam and the final exam.
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:
Social Media Analytics Basics (sl01)
Networks and Graphs (sl02)
Network measures (sl03)
Network models (sl04)
Community structure analysis (sl05)
Diffusion and/or Influence (sl06)
Link Analysis (sl07)
Social Recommendation (sl08) & Local-Based Social Media (sl09)