SOCIAL MEDIA ANALYTICS

Single discipline educational activity

Course Sheet

Course code:

SOMA20

Academic Year of enrolment:

2020

Year of supply:

2020/2021

Type of Course:

Elective Course

Degree:

Course of 2nd Cycle Degree in DIGITAL MARKETING

Disciplinary Sector:

Data Processing Systems

Language:

Italian

Location:

PESCARA

Credits:

Programme Year:

Professor and Collaborators:

MORBIDONI Christian

Cycle:

Third four months

Hours of classroom activity:

Individual study hours:

114

Prerequisites:

The course does not require specific previous knowledge and can be taken by students with any type of background. However, the basic knowledge of software tools for data management (for example Excel and similar) and of the most popular social networks (such as Facebook and Twitter), as well as the knowledge of basic statistical concepts, are facilitating elements to face the course.

Objectives

The course aims to provide students with:
- Understanding the main methods for descriptive and predictive analysis of “social” data, their characteristics and areas of application.
- The ability to select the most suitable tools according to specific needs and to apply them to the analysis of social data in order to derive useful information for the pursuit of marketing objectives.
- Specific concepts and terminology to facilitate communication and collaboration with IT engineers and other “technical” members of the analysis team.
In many enterprise contexts, the analyst is now required to have transversal skills, both in terms of business / marketing and technology (for example, the ability to use advanced software tools and write computer code). This course, while not delving into the technical aspects and not addressing software programming, aims to provide the basic tools and ideas necessary to acquire these multidisciplinary skills.
Finally, the acquired knowledge can be spent in different contexts, such as research in the sociological field.

Contents

The course provides a basic knowledge of representation models and descriptive and predictive data analysis methodologies, with particular reference to data deriving from user interactions with social networking platforms. These data can be of different types, such as time series of actions (posts, retweets, likes, etc.), textual or visual content of posts, social graphs, information on users, etc. The analysis techniques covered range from the measurement and display of quantitative values, to the analysis of networks of social interactions, to the analysis of the text and to the application of Machine Learning techniques. The course will also present a series of software tools that will be used to put into practice some of the techniques discussed. The course includes the analysis of case studies (also through targeted seminars), class exercises and the implementation of a final group project.

Extended Syllabus

Introduction to Data Analytics: plan, collect data, analyze data, present the results of the analysis

Structured and unstructured data, common representation formats, relational model

Characteristics of social data

Methods for extracting data from social networks: API, scrapers and more

Metrics and KPIs to monitor social media

Social network analysis and its applications

Elements of Machine Learning: supervised and unsupervised methods

Elements of text analysis: classification, sentiment, named entities extraction, topic modeling

Data visualization, reporting and interactive dashboards

Examples and case studies: trend analysis, brand awareness, social monitoring / listening, identification of influencers, etc.

The various topics will be accompanied by practical demonstrations using open and / or commercial software tools, such as Tableau, Netlytics, Scikit-learn, Talkwalker, Brandwatch and others.

Recommended Bibliography

Course material will be provided in the form of lectures slides and recommended readings.

Methods of Provision

Conventional

Teaching Methods

The course is articulated in 36 hours of lectures, including theoretical lessons, practical sessions, in-class assignments and seminars.

Evaluation methods

Verification of learning:

The exam will consist of a written test and an oral discussion.
The course involves carrying out a group project in which students will apply the knowledge learned and the tools seen during the course for the analysis of real data.
The project, accompanied by a written report, will also be the subject of evaluation.

Contacts/More Information

ERASMUS students are invited to contact the teacher for detailed program and information.
ERASMUS students can choose to take the discuss the oral exam and write the project report in English or in Italian.

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

Aree