Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| WEB and SOCIAL MEDIA DATA ANALYTICS | COED1114313 | Fall Semester | 3+0 | 3 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | Third Cycle (Doctorate Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Reda ALHAJJ |
| Name of Lecturer(s) | Prof.Dr. Reda ALHAJJ |
| Assistant(s) | |
| Aim | The objective of this course is to provide students with an understanding of concepts and techniques associated with web and social media search, mining and analytics, including concept, principle, architecture, design, implementation, application of web and social media analytic techniques. This course also aims to enable students to discuss and critically evaluate the relative strengths and limitations of the different web search, mining and analytic methods and approaches, to implement and use some of the important web search, mining, and analytics algorithms, apply them to real-world web applications. |
| Course Content | This course contains; Introduction to Web and Social Media, Search, Mining and Web Technologies,Introduction to Web and Social Media, Search, Mining and Web Technologies,Information Retrieval Models: Boolean Model,The Terms and postings lists, Dictionary Data Structures, and Tolerant Retrieval, Index Construction and Compression,Scoring, Term Weighting, and Vector Space Model,Components of an IR system and Performance Evaluation of Information Retrieval Systems,Midterm Week,Introduction Web mining, Association Rules and Sequential Patterns ,Supervised Learning,Unsupervised Learning,Social Network Analysis,Opinion Mining and Sentiment Analysis ,Web Usage Mining ,Project presentations. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Recognizes the web, social media, web and social network data, mining, and analytics methods. | 2 | E |
| 2. Defines how web search engines crawl, index, and rank web content, how network analysis and mining methods work. | 16, 2 | D, F |
| 3. Asseses in-depth knowledge of the fundamental web mining, networks analysis and analytics concepts and techniques. | 12, 14, 21, 6, 9 | A, D, G |
| 4. Describe and utilize a range of techniques for web search, mining, and analytics systems, appreciate the strengths and limitations of various web mining and web search models. |
| Teaching Methods: | 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 21: Simulation Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, D: Oral Exam, E: Homework, F: Project Task, G: Quiz |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to Web and Social Media, Search, Mining and Web Technologies | |
| 2 | Introduction to Web and Social Media, Search, Mining and Web Technologies | |
| 3 | Information Retrieval Models: Boolean Model | |
| 4 | The Terms and postings lists, Dictionary Data Structures, and Tolerant Retrieval, Index Construction and Compression | |
| 5 | Scoring, Term Weighting, and Vector Space Model | |
| 6 | Components of an IR system and Performance Evaluation of Information Retrieval Systems | |
| 7 | Midterm Week | |
| 8 | Introduction Web mining, Association Rules and Sequential Patterns | |
| 9 | Supervised Learning | |
| 10 | Unsupervised Learning | |
| 11 | Social Network Analysis | |
| 12 | Opinion Mining and Sentiment Analysis | |
| 13 | Web Usage Mining | |
| 14 | Project presentations |
| Resources |
| • Social Media Data Mining and Analytics, Gabor Szabo, Gungor Polatkan, P. Oscar Boykin, Antonios Chalkiopoulos, 2018, Wiley • Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites 1st Edition, Matthew A. Russell, Oreilly. • Mark Levene, An Introduction to Search Engines and Web Navigation, Pearson Education, 2010, ISBN 0321306775 • R. Baeza-Yates, B. Ribeiro-Neto. Modern Information Retrieval: the concepts and technology behind search. Addison-Wesley, 2011. • Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. • Soumen Chakrabarti, Mining the Web: Discovering Knowledge from Hypertext Data, Morgan-Kaufmann Publishers, 2003, ISBN 1-55860-754-4 • Pierre Baldi,Paolo Frasconi, Padhraic Smyth, Modeling the Internet and the Web, John Wiley and Sons Ltd, 2003, ISBN 0470849061 |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications. | X | |||||
| 2 | Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas. | X | |||||
| 3 | Evaluate and use new information within the field in a systematic approach and gain advanced level skills in the use of research methods in the field. | X | |||||
| 4 | Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field. | X | |||||
| 5 | Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals. | X | |||||
| 6 | Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements. | ||||||
| 7 | Independently perceive, design, apply, finalize and conduct a novel research process. | X | |||||
| 8 | Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level. | X | |||||
| 9 | Critical analysis, synthesis and evaluation of new and complex ideas in the field. | X | |||||
| 10 | Recognizes the scientific, technological, social or cultural improvements of the field and contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 3 | 42 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 10 | 2 | 20 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 8 | 10 | 80 | |||
| Quiz | 6 | 3 | 18 | |||
| Midterm Exam | 1 | 30 | 30 | |||
| General Exam | 1 | 50 | 50 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 240 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(240/30) | 8 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| WEB and SOCIAL MEDIA DATA ANALYTICS | COED1114313 | Fall Semester | 3+0 | 3 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | Third Cycle (Doctorate Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Reda ALHAJJ |
| Name of Lecturer(s) | Prof.Dr. Reda ALHAJJ |
| Assistant(s) | |
| Aim | The objective of this course is to provide students with an understanding of concepts and techniques associated with web and social media search, mining and analytics, including concept, principle, architecture, design, implementation, application of web and social media analytic techniques. This course also aims to enable students to discuss and critically evaluate the relative strengths and limitations of the different web search, mining and analytic methods and approaches, to implement and use some of the important web search, mining, and analytics algorithms, apply them to real-world web applications. |
| Course Content | This course contains; Introduction to Web and Social Media, Search, Mining and Web Technologies,Introduction to Web and Social Media, Search, Mining and Web Technologies,Information Retrieval Models: Boolean Model,The Terms and postings lists, Dictionary Data Structures, and Tolerant Retrieval, Index Construction and Compression,Scoring, Term Weighting, and Vector Space Model,Components of an IR system and Performance Evaluation of Information Retrieval Systems,Midterm Week,Introduction Web mining, Association Rules and Sequential Patterns ,Supervised Learning,Unsupervised Learning,Social Network Analysis,Opinion Mining and Sentiment Analysis ,Web Usage Mining ,Project presentations. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Recognizes the web, social media, web and social network data, mining, and analytics methods. | 2 | E |
| 2. Defines how web search engines crawl, index, and rank web content, how network analysis and mining methods work. | 16, 2 | D, F |
| 3. Asseses in-depth knowledge of the fundamental web mining, networks analysis and analytics concepts and techniques. | 12, 14, 21, 6, 9 | A, D, G |
| 4. Describe and utilize a range of techniques for web search, mining, and analytics systems, appreciate the strengths and limitations of various web mining and web search models. |
| Teaching Methods: | 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 21: Simulation Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, D: Oral Exam, E: Homework, F: Project Task, G: Quiz |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to Web and Social Media, Search, Mining and Web Technologies | |
| 2 | Introduction to Web and Social Media, Search, Mining and Web Technologies | |
| 3 | Information Retrieval Models: Boolean Model | |
| 4 | The Terms and postings lists, Dictionary Data Structures, and Tolerant Retrieval, Index Construction and Compression | |
| 5 | Scoring, Term Weighting, and Vector Space Model | |
| 6 | Components of an IR system and Performance Evaluation of Information Retrieval Systems | |
| 7 | Midterm Week | |
| 8 | Introduction Web mining, Association Rules and Sequential Patterns | |
| 9 | Supervised Learning | |
| 10 | Unsupervised Learning | |
| 11 | Social Network Analysis | |
| 12 | Opinion Mining and Sentiment Analysis | |
| 13 | Web Usage Mining | |
| 14 | Project presentations |
| Resources |
| • Social Media Data Mining and Analytics, Gabor Szabo, Gungor Polatkan, P. Oscar Boykin, Antonios Chalkiopoulos, 2018, Wiley • Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites 1st Edition, Matthew A. Russell, Oreilly. • Mark Levene, An Introduction to Search Engines and Web Navigation, Pearson Education, 2010, ISBN 0321306775 • R. Baeza-Yates, B. Ribeiro-Neto. Modern Information Retrieval: the concepts and technology behind search. Addison-Wesley, 2011. • Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. • Soumen Chakrabarti, Mining the Web: Discovering Knowledge from Hypertext Data, Morgan-Kaufmann Publishers, 2003, ISBN 1-55860-754-4 • Pierre Baldi,Paolo Frasconi, Padhraic Smyth, Modeling the Internet and the Web, John Wiley and Sons Ltd, 2003, ISBN 0470849061 |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications. | X | |||||
| 2 | Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas. | X | |||||
| 3 | Evaluate and use new information within the field in a systematic approach and gain advanced level skills in the use of research methods in the field. | X | |||||
| 4 | Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field. | X | |||||
| 5 | Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals. | X | |||||
| 6 | Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements. | ||||||
| 7 | Independently perceive, design, apply, finalize and conduct a novel research process. | X | |||||
| 8 | Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level. | X | |||||
| 9 | Critical analysis, synthesis and evaluation of new and complex ideas in the field. | X | |||||
| 10 | Recognizes the scientific, technological, social or cultural improvements of the field and contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |