Métodos Analíticos para Redes Sociais


Social media's rapid growth has given mass consumers a powerful tool to create knowledge and propagate opinions. Simultaneously, social media has created an unprecedented opportunity for organizations to engage in real-time interactions with consumers. In addition, the size and richness of social media data have provided organizations with an unusually deep reservoir of consumer insights to transform business and marketing operations.

The Social Media Analytics course will use a multidisciplinary approach that combines the triad “social network analysis, text mining, and data mining” to help students grasp the analytics tools to leverage social media data. Students will be introduced to and apply tools such as data collection, sentiment analysis, topic modeling, social network analysis, and influencers' identification.

Caracterização geral





Professor responsável

Nuno Miguel da Conceição António


Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês


Familiarity with the main theme of the course is not required. However, it is highly recommended that the students have knowledge of Statistics, Algebra, and Python as well as good skills as computer users.

Students without previous training or experience with Python should complete the three following Datacamp online courses before the third week of this course (first practical class):  Introduction to Python, Intermediate Python, and Data manipulation with pandas. The instructor will provide information on how to have free access to the Datacamp platform.


Método de ensino

The course is based on theoretical and practical classes. Several teaching strategies are applied, including presentation of slides, step-by-step instructions on approaching practical examples, and questions and answers. The practical component is oriented towards exploring the tools introduced to students (Brandwatch, Fanpage Karma, Python, and Gephi) and the development of the final project.

Método de avaliação

Due to the application-based design of the course, evaluation is continuous and applies to both the theory and practical components. There is no “one only exam” with a single weight of 100%.

All evaluation grades are on a scale of 0-20. The final course grade is calculated based on the following weights:

  • Group Brandwatch project: 10%
  • Group data collection project: 10%
  • Group final project:
    • Presentation/discussion: 10%
    • Materials (datasets, code, etc.) and report: 30%
    • The minimum grade is 8.0
  • Individual exam:
    • 1st season or 2nd season: 40% weight
    • With consultation of materials
    • The minimum grade is 8.0

All submissions should be made via Moodle. Submissions after the deadline will be rejected.


  • LU1. Introduction to Social Media Analytics
  • LU2. Introduction to Social Media listening tools: Brandwatch and Fanpage Karma
  • LU3. Data collection
  • LU4. Introduction to Text Mining
  • LU5. Graphs essentials
  • LU6. Network measures
  • LU7. Network visualization tools: Gephi