Digital Communication and Social Media
A.Y. 2025/2026
Learning objectives
The course aims to provide students with an introduction to digital social media, giving them an overview of the purposes, processes and technologies for communicating and interacting on social media platforms. The course illustrates the main tools for the extraction and analysis of data generated dynamically on these platforms. It also includes an overview of the fundamentals of digital marketing and the multiple channels through which companies use digital communication and social media technologies.
Expected learning outcomes
By the end of the course, students will have an understanding of communication processes and interaction and analysis tools in the frame of digital social media. They will also have developed the necessary skills required of mediators in their potential work environments.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
The course covers the following topics:
· How the Web and Search Engines Work
· Ranking Factors and Introduction to SEO
· Social Algorithms - The Social Graph
· Social Algorithms - Recommendation Systems and Personalized Feeds
· Structure of Online Data
· Online Data Collection and APIs
· Social Media Data Collection
· Data Cleaning and Preparation
· Legal and Ethical Aspects of Digital Data
· Automated Text Analysis and AI Tools
· Metrics and KPIs for Digital Data
· Data Visualization
· How the Web and Search Engines Work
· Ranking Factors and Introduction to SEO
· Social Algorithms - The Social Graph
· Social Algorithms - Recommendation Systems and Personalized Feeds
· Structure of Online Data
· Online Data Collection and APIs
· Social Media Data Collection
· Data Cleaning and Preparation
· Legal and Ethical Aspects of Digital Data
· Automated Text Analysis and AI Tools
· Metrics and KPIs for Digital Data
· Data Visualization
Prerequisites for admission
As an introductory course designed for non-computer science students, no prior programming skills are required.
Basic familiarity with computer use, web browsing, and spreadsheet tools (e.g., Excel/Google Sheets) at a user level is sufficient.
Basic familiarity with computer use, web browsing, and spreadsheet tools (e.g., Excel/Google Sheets) at a user level is sufficient.
Teaching methods
The course will be delivered in Italian and will adopt the following approach:
· Lectures: Presentation of theoretical concepts (algorithms, infrastructures) supported by slides.
· The lectures will be complemented by practical simulations.
· Lectures: Presentation of theoretical concepts (algorithms, infrastructures) supported by slides.
· The lectures will be complemented by practical simulations.
Teaching Resources
Given the rapidly evolving nature of the field, the primary study materials include:
· Slides and handouts shown during lectures and made available on the course website (myAriel).
· Technical tutorials and documentation for the software used (provided by the instructor).
Suggested (optional) readings for further study:
· Data Analytics per tutti. Imparare ad analizzare, visualizzare e raccontare i dati.
Andrea De Mauro. Apogeo, 2022. ISBN: 978-8850335947.
There are no differences in the reference materials for non-attending students.
· Slides and handouts shown during lectures and made available on the course website (myAriel).
· Technical tutorials and documentation for the software used (provided by the instructor).
Suggested (optional) readings for further study:
· Data Analytics per tutti. Imparare ad analizzare, visualizzare e raccontare i dati.
Andrea De Mauro. Apogeo, 2022. ISBN: 978-8850335947.
There are no differences in the reference materials for non-attending students.
Assessment methods and Criteria
· Assessment of fundamental knowledge is carried out through a written exam that includes multiple-choice questions on all topics covered in class.
The exam is graded on a scale from 0 to 31.5, with 18 as the minimum passing score.
The resulting score corresponds to the grade out of 30 (the score of 31.5 is recorded as 30 with honors).
Registration for the final exam and communication of results are managed through the university exam system.
No alternative assessment methods are provided for non-attending students.
The exam is graded on a scale from 0 to 31.5, with 18 as the minimum passing score.
The resulting score corresponds to the grade out of 30 (the score of 31.5 is recorded as 30 with honors).
Registration for the final exam and communication of results are managed through the university exam system.
No alternative assessment methods are provided for non-attending students.
INF/01 - INFORMATICS - University credits: 6
Lessons: 40 hours
Professor:
Quattrocchi Giovanni Ennio
Educational website(s)
Professor(s)