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Social Media Analytics

2018/2019
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты
Статус:
Курс по выбору
Когда читается:
3-й курс, 1, 2 модуль

Преподаватели


Нагорный Олег Станиславович

Course Syllabus

Abstract

The discipline is based on the online course “Social Media Analytics: Using Data to Understand Public Conversations” of Digital Media Research Centre, Queensland University of Technology (Australia) (https://www.futurelearn.com/courses/social-media-analytics/).
Learning Objectives

Learning Objectives

  • to analyse social media data and consider how such analyses may be supported by other methods
Expected Learning Outcomes

Expected Learning Outcomes

  • be able to work with information: search, evaluate, combine sources, etc
  • be able to work in an international environment
  • be able to preprocess and analyze data
  • be able to plan and conduct public opinion and marketing studies
  • be able to use data gathering, preprocessing and analysis methods in the decision-making process
Course Contents

Course Contents

  • The role and structures of social media conversations
    The lesson supports topics of the online course: ‘Understanding #conversations’, ‘Gathering Twitter data’. • Social media in daily live, business, government and science. • VK and Twitter: similarities and differences. • Gathering data from VK: introduction to VKMiner.
  • Methods for and implications of gathering data
    The lesson supports topics of the online course: ‘Twitter metrics’ and ‘Making sense of data’. • Exploratory data analysis. • VKMiner tutorial. • Data preprocessing.
  • Key metrics used for analysing Twitter
    The lesson supports topics of the online course: ‘Twitter metrics’ and ‘Making sense of data’. • The key user metrics and their computation for Twitter and VK. • Cluster analysis: k-means, hierarchical clustering. • Introduction to Orange.
  • Methods for identifying trends in social data
    The lesson supports topics of the online course: ‘Twitter metrics’ and ‘Making sense of data’ • Text data: preprocessing, classification, visualization. • The bag-of-words model. • Sentiment analysis with Orange. • Topic modeling.
  • The theory of social networks
    The lesson supports topics of the online course: ‘Social Networks’ and ‘Seeing the big picture’. • Key concepts of network analysis. • Case studies of method applications in scientific studies. • Analysing social structures of VK communities with VKMiner and Orange.
  • Methods for creating and interpreting data visualizations
    The lesson supports topics of the online course: ‘Social Networks’ and ‘Seeing the big picture’. • Visualizing social networks with Orange and Gephi. • Effective communication of results: best practices of data visualization. • Interpreting data visualizations: case studies and practice.
Assessment Elements

Assessment Elements

  • non-blocking Activities in class
  • non-blocking Midterm essay
  • non-blocking Final exam
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.25 * Activities in class + 0.5 * Final exam + 0.25 * Midterm essay
Bibliography

Bibliography

Recommended Core Bibliography

  • Bruns, A., Burgess, J., & Hartley, J. (2013). A Companion to New Media Dynamics. Chichester: Wiley-Blackwell. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=531267

Recommended Additional Bibliography

  • MAHONEY, L. M., & TANG, T. (2016). Strategic Social Media : From Marketing to Social Change. HOBOKEN: Wiley-Blackwell. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1355158
  • Schroeder, R. (2018). Social Theory After the Internet : Media, Technology, and Globalization. London: UCL Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1691623