SOCIAL MEDIA MINING

Hundreds of millions of people are spending countless hours on social media to share, communicate, connect, interact. These activities are a big source of data noisy, free-format, of varying length that are different from the traditional data.

Very frequently the complex systems that produce such big databases are successfully described in terms of networks. In this course we will present and discuss the basic tools used to characterize and analyze large empirical or model networks.

* General skills
Identify the most appropriate techniques for analyzing social networks for each problem
Apply data processing techniques to real data


* Specific skills

Elementary knowledge of the programming language R and its application to the
1) visualization of data coming from social networks
2) data modeling from social networks

Using Markdown languages to write a technical report

Calculus, Statistics, Programming skills.

This course aim:
1 to introduce the concepts of social networks and the various kinds of relation that can occur among members of the network.
2 to explain how to describe social networks, including visualisation.
3 to show how statistical models can be used for social network analysis.
4 to demonstrate the use of software for describing and modelling social networks.

Zafarani, R., Abbasi, M.A, Liu. H. (2014) Social Media Mining, Cambridge University Press

The achievement of the course objectives is assessed through a written exam. The exam consists of four exercises designed to measure
1. the theoretical knowledge of the course topics,
2. the ability to apply them for solving real data problems.
The maximal score for each exercise is 8 points. The final score is the sum of the scores of the four exercises. A total score exceeding 30 corresponds to 30 with honors. During the written test the use of books, notes, or electronic media is *not* allowed.
The examination is performed on the computer

The course is taught through a series of lectures and practical classes. Lectures introduce the concepts and methods with practical classes providing an opportunity for immediate hands on learning though computer based exercises. Participants may benefit from analyzing their own data during the lab sessions. The content of the course can be the basis for a thesis for first cycle degree.

Italian

written