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Mining on Social Networks and the Web

Mining on Social Networks and the Web

Code	16255
Year	3
Semester	S2
ECTS Credits	6
Workload	PL(30H)/T(30H)
Scientific area	Informatics
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.
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.
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
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.
Language	Portuguese. Tutorial support is available in English.