SOCIAL MEDIA MANAGEMENT
Academic Year 2022/2023 - Teacher: ANTONINO FURNARIExpected Learning Outcomes
- Knowledge and understanding: The student will acquire the knowledge of the concepts behind social media and the analysis of their data.
- Applying knowledge and understanding: The student will acquire the practical skills for implementing systems to analyze data extracted from social media.
- Making judgments: Through the laboratories and the projects assigned to the students, they will be able to independently develop solutions that can solve the basic problems which could arise in the world of work.
- Communication skills: the student will acquire the necessary communication skills and the appropriate use of technical language in the general field of social media and data analysis.
- Learning skills: The course aims to delve into theories and techniques useful for the creation of systems for the analysis of multimedia data (images, text, tags, metadata) present in social media. The students will acquire knowledge and skills useful for the analysis of large amounts of multimedia data present in social media. The knowledge acquired will be applied through laboratory and projects. The oral test for passing the exam will be useful for developing the appropriate communication skills.
Course Structure
Frontal lessons and laboratory lessons. Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Required Prerequisites
The course has no specific requirements. Fundamental notions from the following courses will be used:
- Elementi di Analisi Matematica
- Matematica Discreta
- Fondamenti di Informatica
- Programmazione
- Interazione e Multimedia
- Algoritmi
- Metodi Matematici e Statistici
Attendance of Lessons
Recommended
Detailed Course Content
- Social media: definition, features and short history
- APIs and libraries for extrapolation, processing, and visualization of social media data, Web Scraping
- Methods for representing and processing text
- Tools for the advanced analysis of text: bag of visual words model, sentiment analysis, word embeddings
- Recommender systems
- Machine Learning and Pattern Recognition Algorithms Applied to context of Social Media
- Computer Vision algorithms for the processing of images in the context of Social Media
- Python libraries for the analysis of data coming from social media
Textbook Information
- R. Zafarani, M. A. Abbasi, H. Liu, Social Media Mining - An Introduction, Cambridge University Press, 2014
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York: springer.
- C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
- E. Alpaydin, Introduction to Machine Learning, The MIT Press, 2009
- Y. Bengio, I. J. Goodfellow, A.Courville, Deep Learning, Book in preparation for MIT Press, 2015
- Duda, P. E. Hart, D. G. Stork, Pattern Classification (2nd ed.), Wiley, 2000
- R. Szeliski, Computer Vision: Algorithms and Application, Springer 2010
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Introduction to Social Media | Teaching material provided by the teacher and online resources. |
| 2 | Review of fundamental concepts of probablilty theory | Teaching material provided by the teacher and online resources. Parts of chapter 1 of ''Pattern Recognition and Machine Learning'', chapter 2 of ''Deep Learning''. |
| 3 | Social Media API and Web Scraping | Teaching material provided by the teacher and online resources. |
| 4 | Introduction to the fundamental elements of Machine Learning | Teaching material provided by the teacher and online resources. Parts of chapter 1 of ''Pattern Recognition and Machine Learning'', parts of chapter 3 of ''Deep Learning''. |
| 5 | Introduction to text analysis | Teaching material provided by the teacher and online resources. |
| 6 | Classification problem and evaluation measures | Teaching material provided by the teacher and online resources. Section 4.1 of ''An Introduction to Statistical Learning'' |
| 7 | K-Nearest Neighbor classification algorithm | Teaching material provided by the teacher and online resources. Section 2.5.2 of ''Pattern Recognition and Machine Learning'' |
| 8 | MAP e Naive Bayes classification | Teaching material provided by the teacher and online resources. |
| 9 | Linear Regression | Teaching material provided by the teacher and online resources. Section 3.1 of ''Patttern Recognition and Machine Learning''. Chapter 3 of ''An Introduction to Statistical Learning'' |
| 10 | Polinomial and Logistic Regression | Teaching material provided by the teacher and online resources. Section 4.3 of ''An Introduction to Statistical Learning''. Section 7.1 of ''An Introduction to Statistical Learning'' |
| 11 | Bag of Visual Words Model | Teaching material provided by the teacher and online resources. |
| 12 | Recommendation Systems | Teaching material provided by the teacher and online resources. Chapter 9 of ''Mining Massive Datasets'' (http://www.mmds.org/#book) |
| 13 | Advanced text analysis: sentiment analysis, bag of words, word embeddings | Teaching material provided by the teacher and online resources. |
Learning Assessment
Learning Assessment Procedures
Written test, project and oral interview. Learning assessment may also be carried out on line, should the conditions require it.