Магистратура
2022/2023
Интеллектуальный анализ данных в науке о коммуникациях
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
Охват аудитории:
для своего кампуса
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 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 agent-based modelling techniques.
- Students are able to use basic methods of computational text mining.
- 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.
- Students know areas of digital communication studies and are able to formulate a research problem.
Course Contents
- 1) Introduction to Big Data and Social Media Research
- 2) Social Data Science Research Design and Method
- 3) Computational Content Analysis
- 5) Social Network Analysis
Assessment Elements
- Class-workClass-work on seminars
- Project reportA 10-12 page project report which will demonstrate an application of the methods discussed in the class to a dataset and in relation to a social research question. Students are allowed to use any datasets that either they have collected in other courses or are publicly available. The assessment is solely based on the analysis and not the data collection. The limitations of the dataset and the methodologies must be discussed as well. In the assignment you should: - Introduce the dataset that you use and describe the metadata (10%) - Name the analytical methods that you use and discuss their relevance (10%) - Present the results of the analysis (50%) - Discuss the results and explain what they tell us about the dataset under study (20%) - Discuss the limitations in the data and the analytical methods (10%) (Note that the percentages indicate the approximate length of each section not the weight of marks attached. Deviations from these guides are acceptable).
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