DATA, INFORMATION AND SOCIETY 3: SOCIAL NETWORKS AND MEDIA

The course aims at providing an overview of concepts, methods and techniques for data exploration, analysis and visualisation, with particular emphasis on social media data (.e.g, YouTube, Facebook, Twitter). The course will also provide an introduction to network analysis.

- Performing sentiment analysis on social 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
- Network Analysis

Non attending students are invited to contact the instructor.

(This outline may be subject to change)

- Easley, David, and Jon Kleinberg. Networks, crowds, and markets. Vol. 8. Cambridge: Cambridge university press, 2010.
- Wilke, Claus O. Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media, 2019.

The exam consists of a mid-term test to assess theoretical questions about the course content and the development and presentation of a project previously agreed upon with the instructor. The final exam consists of an oral discussion during which students will present the project agreed upon with the instructor. The project's aim is to apply the methods, tools, and techniques learned during the course to develop an original analysis. The project can be carried out in groups. The evaluation will consider the accuracy of the project's execution according to the guidelines, as well as the technical and expressive skills demonstrated in the project's elaboration and discussion. The final grade will be calculated as follows:

70% Project: The accuracy of the project's execution according to the guidelines will determine the evaluation.
20% Mid-term exam: Expressive skills, theoretical knowledge, and language accuracy will contribute to the evaluation.
10% Participation in class

Frontal interactive lessons, seminars, group work

English

oral