Магистратура
2023/2024
Прикладной сетевой анализ
Статус:
Курс обязательный
Направление:
42.04.01. Реклама и связи с общественностью
Кто читает:
Школа коммуникаций
Где читается:
Факультет креативных индустрий
Когда читается:
2-й курс, 1 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
24
Охват аудитории:
для своего кампуса
Преподаватели:
Степнов Михаил Игоревич
Прогр. обучения:
Коммуникации, основанные на данных
Язык:
английский
Кредиты:
3
Контактные часы:
14
Course Syllabus
Abstract
This course represents a primer in social network analysis, a longstanding approach to the generation and analysis of network data. In this course, we introduce many of the fundamentals of social network analysis, from graph theory through personal networks to newer network science approaches, advanced statistical modelling and graph machine learning. After completing the course, the students will be able to apply social network analysis to communication research and projects including influencer marketing and reputation management.
Learning Objectives
- The course is aimed at: learning basic concepts of graph theory and social network analysis; gaining practical skills in network science by analyzing communication processes such as social media influencer marketing and spreading of fake news.
Expected Learning Outcomes
- Students define basic terms and concepts of social network analysis.
- Students explain how differing network analysis metrics relate to each other.
- Students apply analytical tools used in network science on a basic level.
- Students describe how a network can be constructed from an online phenomenon.
- Students design a research project which applies network analysis to practical tasks in advertising, marketing, and PR.
Course Contents
- Major theoretical concepts in social relations: social selection, social influence, and community building.
- Network data and network measures
- Network science approaches
- Practical implications of SNA to communication research
Bibliography
Recommended Core Bibliography
- Fortunato, S. (2009). Community detection in graphs. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.52CD9898
- Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. I. (2017). Big Data and Social Science : A Practical Guide to Methods and Tools. Boca Raton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1353316
- Granovetter, M. (1983). The Strength of Weak Ties: a Network Theory Revisited. Sociological Theory, 201. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=sih&AN=10313288
Recommended Additional Bibliography
- Gildersleve, P., & Yasseri, T. (2017). Inspiration, Captivation, and Misdirection: Emergent Properties in Networks of Online Navigation. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.5F1E0DAE
- Newman, M. E. J. (2006). Modularity and community structure in networks. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.18255B86