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Social Networks Technologies

Code 11113
Year 2
Semester S2
ECTS Credits 6
Workload PL(30H)/T(30H)
Scientific area Informatics
Entry requirements Students must have consolidated programming skills.
Mode of delivery Face-to-face instruction.
Work placements Not applicable.
Learning outcomes Study of the general concepts involved in social media. The leading technologies involved in the creation and maintenance of social networks as well as the creation of applications in this paradigm. Knowledge extraction in social networks — “Social Intelligence.”
In this course, students should acquire a variety of concepts and techniques involved in the design, maintenance, and analysis of instances of social media. Beginning with a brief overview of some conceptual aspects underlying social networks, students will learn to work with development libraries (APIs) for social networking. At the third stage, and taking advantage of previously acquired knowledge, students will learn to analyze and extract relevant information, potentially latent, on a social network. The purpose of this last point focuses on the use of existing technologies, already implemented and accessible to perform this kind of task.
Syllabus Part I - Concepts, Technology, and Foundations
1. Introduction and characterization of the evolution and current state of social networks;
2. Access and Information Manipulation Technologies:
2.1. Review / LEarning / Consolidation of programming languages: Java and Python;
2.2. Study of APIs / SDKs of the main social networks, such as "Twitter", "Facebook", "Google+", LinkedIn, among others;

Part II - Knowledge Discovery in Social Networks
1. Introduction to Data Mining
2. Data Clustering
3. Text Mining
3.1. Human language and text
3.2. Intelligent text manipulation techniques:
3.2.1. Document Indexing
3.2.2. Similarity and document clustering
3.2.3. Document relevance detection
4. Data mining in graphs;
4.1. Social graph mining:
4.2. Centrality Measures
4.3. Community Detection
Main Bibliography Hawker, M., (2011). "The Developer's Guide to Social Programming - Building Social Context Using Facebook, Google Friend Connect, and Twitter API". Pearson Education. ISBN:978-0-32-68077-8
Kumar, S., Morstatter, F., and Liu, H. (2013). "Twitter Data Analytics". Springer Verlag. ISBN:978-1-4614-9371-6
Lutz, M., (2013). "Learning Python, 5th Edition". O'Reilly. ISBN: 978-1449355739
Russel, M., (2019). "Mining the Social Web", 3rd Edition. O'Reilly. ISBN: 978-1491985045
Steven Bird, S., Klein, E., and Loper, E., (2009). "Natural Language Processing with Python". O'Reilly. ISBN: 063-6920516491
Zafarani, R., Abbasi, M., and Liu, H., (2014). "Social Media Mining". Cambridge University Press. ISBN: 9781107018853
Teaching Methodologies and Assessment Criteria The assessment will comprise a written component, in the form of two tests (F1 and F2). There will also be a final assignment (TP) to be carried out during the semester, with expected delivery at the end of the semester. Students will also be evaluated for their work in the practical classes, throughout the semester (AC). In this way, the classification by frequency (CF) is calculated by weighing the classifications obtained in the three components listed above:

CF = 30% F1 + 30% F2 + 30% TP + 10% AC

In the case of CF < 6.0 values the student will not be admitted to the exam, failing the course as a consequence. If CF = 9.5 the student does not need to take the exam. If she/he goes to the exam she/he will always get the best classification. The classification under examination (EC) is calculated as follows:

CF = 70% Exam + 30% TP

In terms of attendance, UBI recommends a minimum of 50% of attendance, both in the practical classes and in the theoretical classes.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2020-05-14

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