TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Bachelor's Degree
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	5
NAME OF LECTURER(S)	Professor Yavuz Ercil
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Know the theories of social network analysis . 2) Can define social media processes. 3) Can develop social network analysis models and applications in social media.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	This course focuses on making sense of social relations with network analysis and evaluating them. Social Network Analysis (SNA) is based on modeling a number of analytical methods and theories and relationships between actors. Social networks play an important role in solving problems between people. In this course, it allows participants to evaluate the structure and dynamics of networks within the framework of communication theories. In addition, competence is developed in order to evaluate the effects of social media, to campaign on a social issue and to produce content using new media technologies.
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction To Social Network Analysis Theory 2nd Week Social Media And Information Networks Concepts: Nodes, Relationships, Adjusting Matrix, Node Degree 3rd Week Social Networking Models 4th Week Network Centrality And Metrics: Bridge, Closeness, Prestige, Eigenvector Centrality, Network Centrality 5th Week Social Media Communities: Virtual Community Structures, Clustering İn Virtual Communities 6th Week Small World Network Models 7th Week Virtual Vision Formation, Coordination And Cooperation 8th Week Midterm Exam 9th Week Applications Of Social Network Analysis 10th Week Student Presentations 11th Week Student Presentations 12th Week Student Presentations 13th Week Student Presentations 14th Week Final
RECOMENDED OR REQUIRED READING	Mark Newman (2010) Networks: An Introduction, Oxford University Press. Blondel, V. D., Guillaume, J.-L., Lambiotte, R. ve Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. Golbeck, J. (2013). Analyzing the social web. Amsterdam: Morgan Kaufmann. John Scott (2000) Network Analysis: A Handbook. Second Edition. Newbury Park CA: Sage. Charles Kadushin (2011) Understanding Social Networks: Theories, Concepts and Findings, First Edition. Oxford University Press. Stanley Wasserman and Katherine Faust (1994) Social Network Analysis: Methods and Applications. First Edition. Cambridge University Press. Tom Valente (2010) Social Networks and Health: Models, Methods and Applications, First Edition. Oxford University Press All the other papers stated in syllabus
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Discussion
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Project 2 30 Attendance 14 10 Total(%) 70 Contribution of In-term Studies to Overall Grade(%) 70 Contribution of Final Examination to Overall Grade(%) 30 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 2 2 Preparation for Quiz Individual or group work Preparation for Final exam 1 40 40 Course hours 14 3 42 Preparation for Midterm exam 1 40 40 Laboratory (including preparation) Final exam 1 2 2 Homework 2 15 30 Total Workload 156 Total Workload / 30 5,2 ECTS Credits of the Course 5
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No