SOCIAL MEDIA AND WEB ANALYTICS

The course aims at providing an overview of concepts, methods and techniques for exploring, analysing and visualising data from main social media platforms (e.g., Facebook, YouTube, Twitter). The course will also provide an introduction to social network analysis.

- Using API to retrieve social media data
- Performing sentiment analysis and emotion detection on user data
- Analysing user interactions on social media
- Extracting conversational topics from social media discussions
- Identifying communities of interest (e.g., echo chambers)

- Basic computing knowledge (basic functions of the operating system, web browsing, e-mail, Office working suites, search engines)
- Good knowledge of R (or equivalent, e.g. Python)

- Introduction to data science and social media analysis
- Working with API
- Data and Information Visualisation
- Topic Extraction
- Sentiment Analysis
- Social Network Analysis

(This outline may be subject to change)

Easley, David, and Jon Kleinberg. Networks, crowds, and markets. Cambridge University Press, 2010.
Barabási, Albert-László. Network Science. Cambridge University Press, 2016

The final exam consists of an oral test lasting approximately 30 minutes, during which students will present and discuss a project previously agreed upon with the instructor. The aim of the project is to apply the methods, tools, and techniques learned during the course, developing an original analysis. The project can be carried out in groups of two students. The evaluation will take into account the accuracy with which the project is carried out according to the guidelines, as well as the technical and expressive skills demonstrated in the elaboration of the project and in the discussion.

Frontal interactive lessons, group work

English

oral