| Code |
16255
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| Year |
3
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| Semester |
S2
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| ECTS Credits |
6
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| Workload |
PL(30H)/T(30H)
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| Scientific area |
Informatics
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Entry requirements |
Good programming skills are required. In particular, we will work with the Python language, both in the examples presented throughout the learning process and in the work to be carried out by the students, both at the level of practical exercises and the final project to be completed.
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Learning outcomes |
This course aims to equip students with a deep theoretical and practical knowledge of the methodologies, tools, and techniques used to automatically collect, analyze, and interpret data from social networks and the web. It is intended that students understand the importance of social networks in modern society, identify and differentiate types of information (structured and unstructured), and be able to access this information effectively.
The course will also empower students in knowledge discovery, including data analysis, text mining, and graph analysis, using machine learning algorithms and natural language processing models. By the end of the course, students are expected to be proficient in applying these techniques to solve real-world problems, capable of extracting valuable insights and identifying significant patterns in the data.
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Syllabus |
1. Social Networks and the World Wide Web (SNWW)
1.1. Introduction and General Characterization of Social Networks
1.1.1. Description and Evolution
1.1.2. Possibilities and Sensitivities
1.2. Information Categories in SNWW
1.2.1. Structured and Unstructured Information
1.2.2. Information of Different Modalities
2. Accessing Information in SNWW
2.1. Protocolled Access
2.1.1. X/Twitter API
2.1.2. Reddit API
2.1.3. Google APIs
2.2. Invasive Access
2.2.1. Use of "Web Scrapers"
2.2.2. Creation of Bots
3. Knowledge Mining
3.1. Data Mining
3.1.1. Introduction
3.1.2. Pre-Processing
3.1.3. Machine Learning Algorithms
3.2. Text Mining
3.2.1. Text Vector Representation
3.2.2. Natural Language Models
3.2.3. Practical Applications and Case Studies
3.3. Graph Mining
3.3.1. Generalities
3.3.2. Centralities
3.3.3. Communities
3.3.4. Probabilities
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Main Bibliography |
[1] Russel, M., (2019). Mining the Social Web, 3rd Edition. O’Reilly.
[2] Vo, L. T. (2019). Mining Social Media: Finding Stories in Internet Data. No Starch Press.
[3] Szabó, G., Polatkan, G., Boykin, P. O., & Chalkiopoulos, A. (2018). Social media data mining and analytics. John Wiley & Sons.
[4] Sarkar, D., Bali, R., & Sharma, T. (2018). Practical machine learning with Python. Apress.
[5] Zafarani, R., Abbasi, M., and Liu, H., (2014). "Social Media Mining". Cambridge University Press.
[6] Steven Bird, S., Klein, E., and Loper, E., (2009). "Natural Language Processing with Python". O’Reilly.
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Teaching Methodologies and Assessment Criteria |
The methodologies promote integrated and applied learning, combining theoretical and practical elements. Theoretical expository classes will be taught to present the syllabus, where key concepts will be explained in detail, accompanied by practical examples that illustrate the application of these concepts. This approach facilitates understanding the principles underlying social networks and web data mining and demonstrates their relevance and applicability in various contexts. To consolidate theoretical learning, practical classes will be conducted on worksheets designed to allow students to apply the studied concepts directly and explore the existing possibilities. Completing a final project constitutes a crucial element of the teaching methodology, allowing students to integrate and apply comprehensively the knowledge acquired throughout the course.
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Language |
Portuguese. Tutorial support is available in English.
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