In recent years, the expansion of Web 2.0 and the emergence of social media as central platforms for social interaction across various domains—from entertainment to politics, and from consumption to work—have significantly altered communication and behavioral patterns, extending beyond personal interactions to political engagement, social movement activism, and marketing strategies.

The digitalized environment has facilitated access to an unprecedented volume of data, known as 'big data.' The development of new methodological and computational tools has enabled the analysis of this data for diverse applications. For instance, digital analytics tools allow organizations, political parties, companies, and influencers to enhance their positioning and effectively reach their target demographics, thereby expanding their membership and voter base, increasing fundraising capabilities, or improving product sales.

This course aims to examine the transformation in communication dynamics induced by digital media proliferation and introduce methodologies for analyzing 'digital trails' for commercial, political, or scientific purposes.

The course structure comprises four main segments:

1. Introduction to the premises of the digital revolution in both scientific and practical research contexts.
2. Overview of analytical methodologies, including statistical, computational, and qualitative approaches, to leverage data effectively, providing a holistic view of the available 'toolkit.'
3. Exploration of data and method applications in contemporary politics and activism, digital marketing, and social sciences.
4. A practical laboratory session where students apply learned methodologies, such as netnography and other advanced analytical techniques, to scientifically investigate and assess digital communication strategies.

The course objectives are threefold:

- Theoretical: to impart knowledge about the digitized landscape;
- Methodological: to provide proficiency in the analytical tools introduced;
- Applicative: to adopt a primarily applicative approach, culminating in a laboratory setting where students gain hands-on experience with the methodological tools discussed.

The program will be discussed in detail during the course and will be subject to adaptations, to meet the learning pace and the prior knowledge of the class. It is proposed to cover the following themes.

• Part one - The context:
  • Internet, digital traces, and big data
  • The networked society
• Part two - The methods:
  • Classical statistical methods
  • Computational methods
  • Qualitative digital methods (netnography)
• Part three - The applications:
  • Social media, Internet, and digital analytics in contemporary politics and activism
  • Digital marketing and optimization
  • Computational social science
• Digital methods laboratory (for attendees)
  • Design:
    • Netnography for defining the concept of an advertising campaign
    • Exploratory analysis of digital data to identify trends and patterns: quantitative analysis and computational text analysis
  • Testing and optimization:
    • A/B testing to choose the best marketing campaign
    • Methods to test the effectiveness of changes in the communication plan of a social media channel

1. Knowledge and understanding: of the opportunities and challenges that digital media pose to social and applied research, research methods for analyzing digital data, and the applications of these methods in the contexts of social research and marketing.

1.1 Students acquire this knowledge especially during lectures and the study of reference materials, through theoretical explanations and practical examples. These skills will be reinforced through individual hands-on exercises and the completion of a group project that will confront them with a concrete research problem using real digital data.

2. Applying knowledge and understanding: to address the analysis of digital data to support practical decisions (for example, choosing between different communication strategies) with a methodologically systematic approach and critical sense, recognizing their potential and limitations.

2.1 Students acquire this knowledge during lectures, through individual hands-on exercises, and the completion of a group project that will confront them with a concrete research problem using real digital data. The exercises and group work will allow students to apply the knowledge they have acquired to a practical problem of their interest, agreed upon with the teacher.

3. Making judgments: to formulate hypotheses and research questions based on scientifically grounded information and digital data, interpreting them rigorously.

3.1 Students acquire this knowledge during explanatory lectures, through individual hands-on exercises, and the completion of a group project that will confront them with a concrete research problem using real digital data. To conduct the research, they will have to conceptualize the problem, gather relevant scientific information by consulting scientific literature on the topic, and formulate hypotheses and expectations based on these, which they will test in light of the results of their analyses.

4. Communication skills: to clearly communicate knowledge, ideas, problems, and solutions to both specialist and non-specialist audiences.

4.1 Students acquire this knowledge during explanatory lectures, through individual hands-on exercises, and the completion of a group project that will confront them with a concrete research problem using real digital data. In particular, the lessons will clarify the structure of scientific reasoning and argumentation by providing a model that is easy to understand and implement. They will then prepare another written work (paper) and a public presentation (in class) of their research results, answering questions from other students and the teacher.

5. Learning skills: to study and learn independently based on existing scientific knowledge.

5.1 Students acquire this knowledge during explanatory lectures that will guide them in consulting, reading, and analyzing scientific literature. The knowledge will be reinforced through individual hands-on exercises and the completion of a group project that will require them to define expectations (hypotheses and research questions) based on the scientific literature related to their specific research problem.

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

No activities are planned to support teaching. However, ongoing assessment activities are planned for attending students. These assessments will enable students to ascertain their understanding of the fundamental elements of the course syllabus.

Teaching

2 weekly appointments of 3 hours each. Lecture, class discussion and project work.

Innovative teaching methods

The course adopts innovative teaching practices such as Problem-Based Learning and Learnin by Doing: students work in small groups to solve concrete problems and complete a digital methods project agreed with the instructor. In addition, in-class exercises are offered to apply theoretical knowledge to specific cases of digital data analysis.

Course books

• Slides provided by the lecturer during class and made available on Blended
• The following texts:
  • Salganik, M. J. (2020). Bit By Bit: La ricerca sociale nell'era digitale. Società editrice il Mulino. https://www.mulino.it/isbn/9788815284907
    • English version: Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press. https://press.princeton.edu/books/paperback/9780691196107/bit-by-bit).
  • Rogers, R. (2016). Metodi digitali: fare ricerca sociale con il web. BolognaIl Mulino. https://www.mulino.it/isbn/9788815265586
    • English version: Rogers, R. (2013). Digital methods. MIT press. https://direct.mit.edu/books/book/3718/Digital-Methods

Assessment

Verification of learning will be:

• through project work, prepared gradually during the course and handed in according to agreed deadlines (60%). 

• The completed project work produced at the end of the course will be discussed in a short presentation to the class (during the exam calls), to assess both the student's learning of the content and his or her ability to present, rework and argue (10%).

• The project should show mastery of the theoretical and methodological knowledge presented during the course, which will be the subject of some specific questions during the presentation (30%).

Will give rise to excellent evaluations: the student's possession of good critical and in-depth skills; the ability to link together the main themes addressed in the course; the use of appropriate language with respect to the specificity of the discipline. Will give rise to fair evaluations: the student's possession of a mnemonic knowledge of the contents; a relative critical ability and ability to connect the topics covered: the use of appropriate language. Will give rise to sufficient evaluations: the student's attainment of a minimal knowledge of the topics covered, despite some formative gaps; the use of inappropriate language. Will give rise to negative evaluations: difficulty in the student's orientation to the topics addressed in the examination texts; formative gaps; the use of inappropriate language.

The group paper will be subject to verification using the anti-plagiarism system in use at the university. Cases of plagiarism will result in a failing grade.

International students are welcome and will have the opportunity to write and present the project in English. International students should make contact with the lecturer at the beginning of the course to arrange the mode of examination in English.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

Attendance

Non-frequenting students should take and stay in contact with the lecturer to agree on the empirical project they will work on and hand it in according to the agreed deadlines (see item 1 in the “Assessment Methods” section).

This course does not differentiate between “attending” and “non-attending” students with regard to teaching methods, attendance obligations, course books or assessment.