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NCRM/Exeter Computational Communication Methods Spring School: 6-14 April 2022

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 2022.

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 6-14 April 2022 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

“Collecting and analysing social media data” presented by Dr Iulia Cioroianu (University of Bath)

 

This workshop provides an introduction to the main computational methods used to access, download and store social media data, as well as an overview of the methods used to analyse this type of data. You will learn how to use Twitter's APIs to collect tweets and user details, and what the latest Facebook data collection and analysis opportunities are. We will start by discussing different options for accessing social media data, cleaning and processing it. We will then cover basic text analysis notions and a variety of data analysis methods which are commonly applied to online text and network data.

Prerequisites: Basic knowledge of R.

 

Presented by Dr Pikakshi Manchanda

 

From self-driving cars, deep fakes, and fraud detection systems to voice-enabled TV remote controls, deep learning (DL) has gradually become a part of modern technology that influences our day-to-day lives. As Artificial Intelligence (AI) continues to evolve and address complex problems, its therefore crucial to understand the principles of deep learning which drive AI.

The aim of this workshop is to discuss the fundamentals of deep learning including important technical topics such as perceptron, activation functions, gradient descent, backpropagation as well as understand the ethical implications of deep learning and AI. The workshop will also provide insights on various types of neural networks, how to build a neural network (using Python libraries) during the hands-on session, evaluation metrics for DL networks, as well as present real-world applications of deep learning, for instance, in the field of computer vision and text analysis.

This workshop is suitable for early career researchers including PhD students and post-docs and anyone who is interested in learning more about Deep Learning and Artificial Intelligence in general.

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.

This additional session will be accessible to participants via recorded video link

Analysing social media data with psycholinguistic techniques

Instructor: Dr. Massimo Stella

Format: 45 mins video + Q/A by email

Psycholinguistics investigates the mechanisms enabling psychological reflections of language in the human mind. This asynchronous video will provide a quick overview of psycholinguistic techniques and models of relevance for communication analysis within the specific context of social media. Emphasis will be given to word norms and their interpretation in language, like concreteness or imageability. We will also explore psychological models going beyond mere sentiment analysis, like the circumplex model of affect and Plutchik’s wheel of emotions. Finally, we will discuss how to adopt these tools to reconstruct the meaning and perceptions surrounding non-words like hashtags, which are particularly relevant for discourse analysis.

Bursaries

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.