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NCRM/Exeter Computational Communication Methods Spring School: 17-27th April 2023

NCRM/Exeter Computational Communication Methods Spring School

NCRM/Exeter Computational Communication Methods Spring School

NCRM/Exeter Computational Communication Methods Spring School

NCRM/Exeter Computational Communication Methods Spring School


Researchers interested in computational social science will be given the chance to learn new skills at a spring school in April 2023.

The NCRM/Exeter Computational Communication Methods Spring School will provide training at introductory and advanced levels, catering for both social scientists and data scientists.

The school will take place at the University of Exeter over two 4-day sessions on 18-27 April 2023 (with an additional Intro to Python day on 17th April) and is co-sponsored by:-

  • IDSAI Computational Social Science
  • Social Data Science Group, Turing Institute
  • Exeter Q-Step

The programme will cover multiple computational approaches, such as machine learning and network analysis, and their application to communication research looking at text, images and social media data.

World-leading experts will deliver workshops, seminars and demonstrations, help desks will offer one-to-one consultations and there will be opportunities for more informal networking.

This Spring School is open to University of Exeter students & staff, and non-University delegates alike. We welcome applications from Masters students, PhDs, post-doctoral researchers, early career researchers, and also more senior researchers and lecturers.

Please note the programme will take place in person only on the University of Exeter's Streatham Campus.

Programme Overview

4 day introductory session presented by Dr Travis Coan and Dr Chico Camargo


Differently from traditional software, artificially intelligent software can improve performance upon ingesting increasing quantities of data. This module will introduce you to the core concepts that are needed to understand the field of Machine Learning. You will engage with the theory and gain practical experience through a series of practical workshops. In this module we will emphasize the notion and importance of data and you will learn how machines can deal with different types of data sources, ranging from text to images to networks, and all sorts of metadata.

This course will also provide a more in-depth introduction to the use of natural language processing in computational communication research. You will learn how to apply various supervised, semi-supervised, and unsupervised machine learning methods for analysing textual data. You will also be introduced to the topics of language modelling, semantic similarity, and recent advances in transfer learning. Through a series of lectures and practical applications, this section of the course aims to provide you with the tools to use “text as data” in your own research projects.

Week 2: Advanced Sessions

Abstract TBC

Presented by Dr Diogo Pacheco (University of Exeter)


Presented by Dr Constantine Boussalis


As we transcend the the digital revolution, digital images are being generated and shared at an astounding pace. To give a sense of the scale, it is estimated that over 14 billion images are shared daily on social media platforms, and over 136 billion images have been indexed on Google Image Search to date.


This workshop provides an introduction to the application of state-of-the-art computational methods to analyse the content of digital images at large scales. In particular, the workshop will offer a theoretical review of important algorithms, such as convolutional neural networks,  that have proven to be remarkably effective in a wide range of applied social science research objectives involving the use of images as data. Beyond theory, the workshop will also provide practical instruction on how to apply machine learning methods in Python to perform a number of common image-as-data tasks, such as automatically detecting faces in an image, recognizing the identity of a face, as well as automatically assigning images into coherent clusters.


The workshop is particularly designed for early stage researchers and postgraduate students who are interested in learning more about how to use computers to analyse large collections of digital images.

Presented by Dr Debora Nozza (Bocconi University)


Abstract TBC

Presented by Dr Nicolas Gold

The contemporary research landscape is one in which online data plays a significant role in many disciplines.  Whilst often appearing to be a straightforward and easily accessible source of research data, there are a range of ethics issues to address including privacy, rights, expectations, information, and consent.  These are not necessarily straightforward to deal with in the online context.  In this talk I will discuss some of the issues involved, present a framework that may be helpful to researchers addressing these issues in their work, and review some example situations.

Participants can choose to join either/both weeks 1 and 2, or in week 2 you are welcome to simply join for individual days.

Sessions will be suitable for PhD and postdoctoral researchers, as well as early-career and senior academics. In addition to training, there will be opportunities for participants to develop new interdisciplinary research collaborations.


Bursaries will be available to cover some or all of the course fees, and will be available in the following ways:

  • University of Exeter students and staff: will be automatically considered for a busary from the University of Exeter
  • Non-University of Exeter participants: you can apply for a bursary from NCRM, with priority given to Early Career Researchers. To check eligibility and make a NCRM bursary application please click here

Programme Fees

Standard Course Fees University of Exeter Students & Staff External Participants
Per Week £100 £200
Per Day £40 £80

Applications are now closed.

If you would like to enquire about on campus accommodation please contact Event Exeter to check availability and costs.