Bachelor 2018/2019

Social Media Analytics

Type: Elective course (Sociology and Social Informatics)

Area of studies: Sociology

Delivered by: Department of Sociology

Where: Saint-Petersburg School of Social Sciences

When: 3 year, 1, 2 module

Mode of studies: distance learning

Instructors: Oleg Stanislavovich Nagornyy, Sergei Pashakhin

Language: English

ECTS credits: 3

Contact hours: 12

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

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

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

• 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

• Activities in class
• Midterm essay
• Final exam

Interim Assessment

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

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