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Social Media Data Mining

Eastern Mediterranean University Faculty of Communication and Media Studies Department of New Media and Communication

Course Outline

The course will provide an overview of the fundamental concepts related to data sources, data storage methods, data organisation, and processes involved in the analysis of large data sets. It will also address the ethical considerations associated with big data analytics. Additionally, it will equip students with practical skills in data scraping, organisation, processing, and interpretation, with a focus on their application in field studies.

Course Completion Requirements

The course will examine the nature of data, the methods of its storage and cleansing, and the design of data transfer systems that facilitate meaningful data output. It will also integrate theoretical and practical studies of data processing.

Exams and Projects

A practice exam, midterm and final project will be applied within the scope of the course.

  • Practices %35
  • Midterm project %15
  • Final project %50

Format of the Lesson

A teaching methodology based on practice and project work will be employed. The practices have been designed to facilitate the preparation for projects, thereby facilitating the practical comprehension of the theoretical information presented.

Course Outcomes

Upon completion of the course, students will possess a comprehensive understanding of the fundamental concepts related to data, including its definition, sources, acquisition methods, storage, and processing techniques to generate meaningful insights. Additionally, they will develop the ability to interpret data from diverse fields of study.

Important Notes

It should be noted that students who do not attend a minimum of two practical courses and plagiarise will be deemed to have failed the course, despite having received a passing grade.

Useful Links

Data Visualization: Flourish Studio

Weekly Courses Content

1. Week

Social Media and Data Mining Introduction

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons.

2. Week

Introduction to Data Mining Techniques

Han, J., Pei, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques (Chapter 1-2).

3. Week

Social Media Data Structures

Gundecha, P., & Liu, H. (2012). Mining social media: A brief introduction. INFORMS Journal on Computing.

4. Week

Web Scraping Techniques

Mitchell, R. (2015). Web Scraping with Python. O’Reilly Media.

5. Week

Big Data and Social Media

Zikopoulos, P., & Eaton, C. (2011). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data.

6. Week

Text Mining

Aggarwal, C. C., & Zhai, C. (2012). Mining Text Data. Springer. (Chapter 3-4)

7. Week

Emotion Analysis

Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval.

8 - 9. Weeks

Midterms

10. Week

Social Network Analysis

Scott, J. (2017). Social Network Analysis. Sage Publications. (Chapter 2-3)

11. Week

Visual Content Mining

Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2008). Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys.

12. Week

Data Privacy and Ethics

Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society.

13. Week

Social Media Marketing Strategies

Tuten, T. L., & Solomon, M. R. (2017). Social Media Marketing. Sage Publications.

Elmazi, L., & Günay, A. (2019). Big data-driven marketing: A new frontier. Journal of Marketing Analytics.

14. Week

Finals

Detailed Grades

Practices

%35

It includes the projects given in the course as homework within the scope of data processing. A total of 5 projects will be graded with 7 points each.

Practices

Evaluation Criteria

  • Data input
  • Data extraction
  • Visualisation
  • Content generation

Midterm Project

%15

Processing the appropriate data for the project file prepared on the determined topics and producing content with this information.

Evaluation Criteria

  • Project file
  • Data file
  • Content file

Final Project

%50

The final project consists of 2 projects. The projects will be done within the framework of the topics covered in the course, 1 by using ready-made data and 1 by collecting and organising data. Content will be produced based on the data from these 2 projects.

Evaluation Criteria

  • Data collection
  • Data cleansing
  • Visualization of data
  • Content production from the resulting data
Class Notes