SOCIAL MEDIA AND WEB ANALYTICS

The course offers an overview of methods and techniques for exploring, analyzing, and visualizing digital data and data from social media platforms. Through an integrated theoretical and practical approach, it also introduces the fundamental concepts of social network analysis, highlighting their usefulness in studying online social dynamics. The course is designed to provide students with operational and analytical skills in the fields of social media studies and digital data analysis.

Knowledge and understanding:
- Understand the fundamental principles related to the management, processing, visualization, and interpretation of large volumes of data.
- Acquire both theoretical and practical knowledge of data science techniques applied to the analysis of web and social media data.
- Become familiar with the main methodologies of social network analysis, understanding their theoretical foundations and applications in digital contexts.

Applying knowledge and understanding:
- Apply methods and tools for managing and analyzing structured and unstructured data collected from the web and social media platforms.
- Conduct analyses of social networks and digital content using appropriate software and programming languages.
- Critically interpret the results of such analyses and communicate them clearly and rigorously.

Learning skills:
- Be able to consult, understand, and use technical documentation related to tools and libraries for digital data analysis.
- Develop a critical and reflective approach to data usage and analytical methodologies, with the ability to independently update and deepen knowledge of emerging tools and techniques in the field of social media and web analytics.

A basic knowledge of the following topics is required:
- Programming: fundamental concepts of structured programming and experience with at least one programming language (e.g., Python).
- Statistics: elements of descriptive statistics, basic probability, and essential notions of statistical inference.

- Introduction to data science and social media analysis
- Social Network Analysis
- Working with API
- Data and Information Visualisation
- Topic Extraction
- Sentiment 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 examination and the completion of a project previously agreed upon with the instructor. The oral exam (approximately 30 minutes) is divided into two parts:

1) Theoretical questions and exercises on topics covered during the course (30%): Assessment will be based on the accuracy of the answers, the appropriate use of language, and clarity of expression.
2) Discussion of the project previously agreed upon with the instructor (70%): Students will be expected to present an original analysis applying the methods, tools, and techniques covered in the course. The project can be carried out individually or in groups of up to two people. Evaluation will consider adherence to the guidelines and the technical complexity of the work.

The project discussion is conditional on passing the theoretical part.

oral

Grades 18-21: Sufficient
- Oral exam: Partial understanding of theoretical topics, uncertain expression with language inaccuracies. Answers are fragmented or incomplete.
- Project: Minimal adherence to guidelines, basic analysis with possible methodological errors. Limited technical complexity but sufficient to demonstrate a fundamental application of the covered concepts.

Grades 22-24: Fair
- Oral exam: Correct but not always in-depth answers, adequate language with some inaccuracies. Good ability to apply the topics.
- Project: Analysis consistent with the guidelines, appropriate methodology, but lacking significant depth or originality. Moderate technical complexity.

Grades 25-27: Good
- Oral exam: Precise and well-argued answers, good use of technical language, confident presentation.
- Project: Well-structured and accurate analysis, solid application of course methods and tools. Well-developed technical complexity with elements of originality.

Grades 28-30: Very Good/Excellent
- Oral exam: Complete and well-structured answers, excellent mastery of topics, and appropriate use of technical language. Strong critical and interdisciplinary reasoning skills.
- Project: In-depth and original analysis, rigorous application of methods with potential extensions or improvements. High technical complexity and advanced problem-solving skills.

30 with Honors
- Excellence in both the oral exam and the project, demonstrating advanced critical and analytical skills, flawless presentation, and a particularly innovative contribution in the project.

The course is based on interactive lectures, where theoretical concepts are introduced and explored in depth through practical examples and guided discussions.

The lectures combine:
- Theoretical presentation of the fundamental principles of network analysis and its methodologies.
- Practical examples, used to concretely illustrate the application of the methods and tools covered.
- Student interaction, encouraging active participation through questions, critical reflections, and case study analysis.

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development