The course aims to provide an overview of the methods of studying the Network and digital media, to teach the basics of the programming language for statistical processing "R", to introduce social network analysis, also using the Gephi software, and online communication as it manifests on social media.

The course will be divided into two main parts, a theoretical and a practical one, which will be followed, for attending students, by a project work. In the first part of the course, an introduction will be provided to the characteristics of digital media, to research ethics, and to digital research methods, considering the main network analysis methodologies and some of the main tools used in social research and digital marketing studies. In the second part, R (and the RStudio development environment), a programming language for statistical processing and graphics widely used in academic and business environments, and Gephi, a software for social network analysis, will be introduced. Regarding R, particular attention will be given to its fundamental features and some basic functions for collecting data from social web media and for statistical analysis. The attending students will finally be involved in a short project work aimed at putting into practice the acquired knowledge, to be applied to a case study focused on social media data.

The goal of the course is to provide students with a good understanding of the basic knowledge related to 1) the methodologies for analyzing digital social environments, 2) the collection of data from the web and social media, 3) the exploration of the related datasets through basic statistical analyses, networks, and content using the R language and Gephi software, as well as 4) the critical interpretation and presentation of research results.

1.1 Networked Publics, Social Media Logic, Prosumerism

1.2 Ethics of digital research

1.3 Methods of digital research (API, web scraping, data mining, text mining, social network

analysis, web listening, sentiment analysis, digital ethnography...)

1.4 Overview of free and commercial tools

2.1 Installation of R and RStudio (Integrated Development Environment for R)

2.2 User Interface and Packages

2.3 Data types and structures

2.4 Functions

2.5 Graphics

2.6 Basic statistical analyses

2.7 Data collection via APIs (Twitter)

3.1 Fundamental concepts of social network analysis

3.2 The Gephi software

4.1 Data collection

4.2 Data analysis

4.3 Writing a brief research report

No prerequisites

1.1. Students acquire this foundational knowledge through attending lectures, studying the texts discussed in class, and individual exercises.

2.1. Skills are acquired through study materials and hands-on lab exercises in class.

3.1 Students acquire such basic knowledge through in-class discussions, engagement with the instructor, and exercises using the prescribed software.

4.1 Students acquire these skills through attending lectures, studying the texts, and creating the research report derived from the project work.

5.1 Students acquire such abilities through in-class discussions, exercises, engagement with the instructor, and studying the texts.

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

Teaching

·  Introductory lectures on digital media, digital methods, and research ethics

·  Introductory lectures on R and Gephi

·  Practical exercises with R and Gephi

A personal computer capable of connecting to the Internet is recommended (students can benefit from a Wi-Fi connection from the course lecture rooms) on which to install and practice with the study programs.

Attendance

To be considered attending students, it is necessary to participate in at least three-quarters of the lecture hours (32 hours).

Course books

1.  boyd, d. (2010). Social network sites as networked publics: Affordances, dynamics, and implications. In “A networked self” (pp. 47-66). Routledge. https://www.danah.org/papers/2010/SNSasNetworkedPublics.pdf

2.  Van Dijck, J., & Poell, T. (2013). Understanding social media logic. Media and communication, 1(1), 2-14. https://www.cogitatiopress.com/mediaandcommunication/article/view/70

3.  W. N. Venables, D. M. Smith, & the R Core Team (2020). An Introduction to R (Notes on R: A Programming Environment for Data Analysis and Graphics Version 4.0.2. 2020-06-22). https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf

4.  Oliveira, M., & Gama, J. (2012). An overview of social network analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(2), 99-115. https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1048

Assessment

The assessment of learning for attending students will aim to evaluate the student's understanding of the content and will take place through an individual oral interview based, in part (50%), on the concepts presented in the lecture and on the reference texts for the exam, and in part (50%), on the critical discussion of the research report derived from the project work developed during the course.

·  Possession of good critical abilities and in-depth understanding of the course content, the use of appropriate language with respect to the specificity of the topics covered, and a methodologically correct, precise, and orderly research report will lead to excellent evaluations.

·  Possession of mnemonic knowledge of the content by the student; a relative critical ability, the use of appropriate language, and a decent research report will lead to discrete evaluations.

·  The achievement of a minimal body of knowledge on the topics covered by the student, even in the presence of some gaps in training; the use of not entirely appropriate language and a report with some deficiencies will lead to sufficient evaluations.

·  Student difficulties in orienting themselves with respect to the topics addressed in the exam texts, gaps in training, the use of inappropriate language, and a deficient research report will lead to negative evaluations.

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.

Course books

Textbooks

1.  boyd, d. (2010). Social network sites as networked publics: Affordances, dynamics, and implications. In “A networked self” (pp. 47-66). Routledge. https://www.danah.org/papers/2010/SNSasNetworkedPublics.pdf

2.  Van Dijck, J., & Poell, T. (2013). Understanding social media logic. Media and communication, 1(1), 2-14. https://www.cogitatiopress.com/mediaandcommunication/article/view/70

3.  W. N. Venables, D. M. Smith, & the R Core Team (2020). An Introduction to R (Notes on R: A Programming Environment for Data Analysis and Graphics Version 4.0.2. 2020-06-22). https://cran.r-project.org/doc/manuals/r-release/R-intro.pd

Assessment

The assessment of learning for attending students will aim to evaluate the student's understanding of the content and will take place through an individual oral interview based, in part (50%), on the concepts presented in the lecture and on the reference texts for the exam, and in part (50%), on the critical discussion of the research report derived from the project work developed during the course.

·  Possession of good critical abilities and in-depth understanding of the course content, the use of appropriate language with respect to the specificity of the topics covered, and a methodologically correct, precise, and orderly research report will lead to excellent evaluations.

·  Possession of mnemonic knowledge of the content by the student; a relative critical ability, the use of appropriate language, and a decent research report will lead to discrete evaluations.

·  The achievement of a minimal body of knowledge on the topics covered by the student, even in the presence of some gaps in training; the use of not entirely appropriate language and a report with some deficiencies will lead to sufficient evaluations.

·  Student difficulties in orienting themselves with respect to the topics addressed in the exam texts, gaps in training, the use of inappropriate language, and a deficient research report will lead to negative evaluations.

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.