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NUSC Summer School in Network and Data Science

Mon 20th - Sat 25th June 2022

The NUSC Summer School provides opportunities for those both new to network and data science and those who wish to consolidate or expand existing knowledge in the field. Three distinct courses offer an introduction to social network analysis, a workshop on social media and text-mining with R, and an introduction to relational event modelling. The courses will be provided in an in-person, campus environment, in the iconic UNESCO world heritage site of the University of Greenwich, in London.

The courses are aimed to equip postgraduate students, researchers and social science practitioners with skills to apply in practical projects. This is an in-person event only.

Programme

Each course runs 10:00-16:00 each day:

Doing Research with SNA: Tools, Theories, and Applications June 20th-22nd.
Social Media and Text Mining in R, June 23rd.
Relational Event Models (REMs) for the Analysis of Social Networks: A Hands-on Introduction, June 24th-25th.

Course Descriptions

1. Doing Research with SNA: Tools, Theories, and Applications

Instructor: Bruce Cronin

About:
The goal of the course is to provide attendees with a general overview of the field of social network analysis, confidence in using its key analytical tools in practice, and insight into how it can be used in scholarly practice in the social, economic, managerial and political disciplines. The focus is on research design and how SNA elements can be successfully integrated into a research project, paper, or dissertation. Participants will be introduced to UCINET and Netdraw software via practical exercises

Requirements
All social science backgrounds are welcome, and participants are assumed not to have any previous knowledge of SNA, or of any analytical or statistical software. No previous experience with the software is expected.

At the end of the course participants will be able to:

independently design a research programme requiring SNA in their own field of research
collect and manage network data;
analyse, interpret and visualise fundamental network measures at the individual, group and network level;
confidently use UCINET and NetDraw to perform network analysis and visualise network data.

Instructor
Bruce Cronin is Professor of Economic Sociology at the University of Greenwich, where he is co-director of the Networks and Urban Systems Centre

General references
Borgatti, SP, Everett, MG and Johnson, JC (2018) Analysing Social Networks, 2nd Edition. London: Sage.

2. Social Media and Text Mining in R

Instructor: Dr Mu Yang

About
An introduction to social media analytics and text mining with the R-programming language.

Requirements
Participants should have an elementary knowledge of the R-programming language.

At the end of the course participants will be able to:

Make use of key metrics used for analysing social media,
Undertake sentiment analysis on user-generated-content on social media,
Employ topic modelling for identifying trends in social data

Instructor
Dr Mu Yang is Senior Lecturer in Digital Marketing Analytics at the University of Kent, where she is Interim Director of TIME Research Centre.

3. Relational event models (REMs) for the analysis of social networks: A hands on Introduction

Instructors: Jürgen Lerner and Alessandro Lomi

About
Networks of social relations and communication networks frequently generate information on repeated interaction over time. This information typically takes the form of relational event sequences - streams of time-ordered events connecting social actors. Examples of relational events are common. Conversations, email communication, interaction among members of teams, participation in social gatherings or in peer-production projects, are all examples of interactive settings that may generate observable streams of relational events.

The goal of this workshop is to provide participants with an introduction to relational event modeling - both for dyadic events (having one sender and one receiver) and for "hyperevents" connecting any number of participants. The workshop involve hands-on experience with software specifically designed for specifying end estimating relational event models on actual data, including the open-source software eventnet (https://github.com/juergenlerner/eventnet).

Requirements
The workshop is targeted at participants interested in statistical modeling of networks based on relational event data. Participation to the workshop does not assume any particular prior knowledge or experience with statistical models for social networks. Participants are invited to informally share their own research questions, which may possibly be addressed by a REM analysis, prior to or during the workshop.

By the end of the workshop participants will be able to:

Design and apply REMs in their own empirical study;
Understand variations of REMs including typed or weighted events, multi-mode networks, actor-level or dyad-level attributes;
Implement sampling strategies to fit REM to large event networks;
Understand the foundation of relational hyperevent models for analyzing multicast relational events;
Read, understand and comment on current research papers based on REMs

Instructors
Jürgen Lerner is interim professor for Computational Social Sciences and Humanities at the RWTH Aachen. Alessandro Lomi is a professor at the University of Italian Switzerland (Lugano) where he directs the Social Network Analysis Research (SoNAR) Center

General references
Butts, C. T. (2008). A relational event framework for social action. Sociological Methodology, 38(1), 155-200.
Lerner, J., & Lomi, A. (2022). A dynamic model for the mutual constitution of individuals and events. Journal of Complex Networks, 10(2), cnac004.

For full details and registration, please see:

https://www.gre.ac.uk/bus/events/nusc