Webinar: Social Network Analysis: Fundamental Concepts
1 Sep 2020 2:00 pm - 3:00 pm
Vast swathes of our social interactions and personal behaviours are now conducted online and/or captured digitally. Thus, computational methods for collecting, cleaning and analysing data are an increasingly important component of a social scientist’s toolkit. Social Network Analysis (SNA) offers a rich and insightful methodological approach for uncovering and understanding social structures, relations and networks of association.
This free webinar, organised by the UK Data Service, is the first in a series of three on understanding and using SNA methods for social science research purposes. In this webinar we cover the fundamental concepts and terms underpinning SNA, and demonstrate how network data is structured and differs from more traditional social science datasets (e.g. social surveys). We will also outline a simple analysis of social network data using the Python programming language. As a result of attending this webinar, participants will possess the necessary knowledge and vocabulary to undertake a SNA research project.
Level: Introductory, for individuals with no prior knowledge or experience of social network analysis
Duration: 45 minutes, followed by questions
Audience: Researchers and analysts from any disciplinary background interested in employing network analysis for social science research purposes
Programming language: Python is used to examine the structure of social network data and to perform a simple analysis
Materials: Participants will have access to an interactive online notebook through which they can learn more about SNA and execute Python code
Learning outcomes: Understand fundamental concepts and terms associated with SNA and understand how social network data are structured
Webinar two demonstrates the steps needed to collect and clean social network data, drawing on two examples: Twitter data and administrative data that can be repurposed for social network analysis.
Webinar three rounds off the series by diving into the concepts behind social network methods of analysis and presents some research examples using Python.
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