Магистратура
2019/2020
Интеллектуальный анализ данных в науке о коммуникациях
Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Лучший по критерию «Новизна полученных знаний»
Статус:
Курс обязательный (Коммуникации, основанные на данных)
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:
без онлайн-курса
Прогр. обучения:
Коммуникации, основанные на данных
Язык:
английский
Кредиты:
3
Контактные часы:
28
Course Syllabus
Abstract
The course is aimed at introducing the students with the areas of digital communication research and application of Big Data mining methods to communication studies. The internet is but one of many networks. Every network is different in its own way but there are striking similarities, whether we refer to traffic routing, infectious diseases, friendships on Facebook or gossip on Twitter. This course represents a primer in social network analysis [SNA], 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 and advanced statistical modelling. Each week includes reading summaries and exercises designed to build the student's capacity for network analysis. We conclude the course with a critical interrogation of network analysis to help circumscribe some limits to this otherwise exciting and powerful paradigm. The result is a well-rounded course designed to enable the effective use of networks in research.
Learning Objectives
- The course will familiarise students with the state of network science as a paradigm comprising multidisciplinary approaches to the analysis of relational data. Students will be able to read introductory network metrics and understand how these measures speak to theories of human behaviour as well as put together an original piece of analysis using network data. Students will gain a modest understanding, via the 'sociology of science', as to why network analysis is a highly distributed field where no single software application, journal or conference covers all of the active research on social networks. Students will also learn basic data capture and analysis techniques that can enable them to begin, if not complete, a full social network analysis study.
Expected Learning Outcomes
- Students know areas of digital communication studies and are able to formulate a research problem.
- Students are able to develop a research design and chose a method at the cross-section of social sciences and data science. .
- Students are able to use basic methods of computational text mining.
- Students are able to use basic agent-based modelling techniques.
- Students have a familiarity with the basic terms and concepts of social network analysis; understand how differing network analysis metrics relate both to each other and to academic research questions; are able to describe how a network can be constructed from an online phenomenon; have a clear understanding of some of the various analytical tools used in network science; are able to construct and theorise a research question that employs social network analysis.
Course Contents
- 1) Introduction to Big Data and Social Media ResearchBig data, the real time streams of transactional records of our daily activities, hold major promise for (computational) social science. We discuss and showcase examples of the use of big data and social media data in social systems research.
- 2) Social Data Science Research Design and MethodWe introduce social data science as a new field of research at the cross-section of social sciences and data science. We discuss the research design and the rules and recipes for a successful research project.
- 3) Computational Content AnalysisWith the increase in the size of digitalised textual data and their use in social science and humanities research, the use of computational methods in analysing text is becoming very common. In this course we introduce some introductory techniques to computational text mining.
- 4) Agent-Based Modelling and Network Analysis of Wikipedia ConflictsAgent-based modelling, a fascinating technique for answering social science questions, based on computer simulation of real-world societies and real-world human events.
- 5) Social Network AnalysisSocial network analysis with particular emphasis on research design, data collection and analysis. We take a comparative approach to network topics, such as evaluating different measures of centrality, multiple approaches to clustering and variations on visualisation.
Bibliography
Recommended Core Bibliography
- 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
Recommended Additional Bibliography
- Aral, S., & Van Alstyne, M. (2011). The Diversity-Bandwidth Trade-off. American Journal of Sociology, 117(1), 90–171. https://doi.org/10.1086/661238
- ElAtia, S., Ipperciel, D., & Zaiane, O. R. (2017). Data Mining and Learning Analytics : Applications in Educational Research. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1351385
- 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
- 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
- Granovetter, M. S. (1973). The strength of weak ties. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.27DB449F
- 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
- Yasseri, T., & Bright, J. (2016). Wikipedia traffic data and electoral prediction: towards theoretically informed models. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.B80D8425