Using large scale social media data to measure perceptions towards immigration

Collaborators: Eduardo Graells-Garrido¹, Marzia Rango², Michael Mahony³, Yerka Freire-Vidal⁴, Bruce Newbold⁵ and Niklas Sievers²

¹ Barcelona Supercomputing Center, Barcelona, Spain.
² Global Migration Data Analysis Centre (GMDAC), International Organization for Migration (IOM), Berlin, Germany.
³ Geographic Data Science Lab, University of Liverpool, Liverpool, United Kingdom.
⁴ Universidad del Desarrollo, Santiago, Chile.
⁵ McMaster University, Hamilton, Canada.

Currently this project has four active areas of work:

• Using Twitter to Track Immigration Sentiment During Early Stages of the COVID-19 Pandemic. This work package involves a collaboration with IOM. Outputs from this work package are currently in review and should become available in the coming weeks. We have made the data and code available via a Github repository registered on the Open Science Framework. As part of this work package, we have also written a contribution for a practitioner guide book which will be published by the IOM. We have also published a Github repository, to make available the data and code.

• Determining the Key Sources, Speed and Evolution of Anti-migration Sentiment on Social Media. This work package is part of the Consumer Data Research Centre, Masters Dissertation Scheme and also involves a collaboration with IOM. Two students have been recruited and will be working on their dissertation projects during this summer

• Understanding Attitudes Towards Immigration Using Twitter. This work package is led by Yerka and aims to propose a novel framework to measure attitudes towards immigration in the digital world. Outputs from this work package are in the final stage

• Monitoring Online Attitudes Towards Immigration in Canada During the COVID-19 Pandemic. This work package aims to identify the extent and themes of anti-immigration sentiment against migrant groups during the COVID-19 pandemic. This work package is in the early stages of data collection

• Monitoring Online Attitudes Towards Immigration in Canada During the COVID-19 Pandemic. This work package aims to identify the extent and themes of anti-immigration sentiment against migrant groups during the COVID-19 pandemic. This work package is in the early stages of data collection

Also, Eduardo and I were nominated as an expert and project leader for the [2020 BIGSSS Summer School in Computational Social Science on Social Cohesion in Groningen, Netherlands.] (https://bigsss-css.jacobs-university.de/) The Summer School was postponed due to the pandemic. There are plans to run a short virtual session in the summer this year (2021) and the Summer School in 2022. I will be leading a project on using twitter data and computational approaches to measure social cohesion and attitudes towards immigration. The project seeks to offer participants of the BIGSSS Summer Schools in Computational Social Science the opportunity to build on our experience in the use of Twitter data and machine learning to model and enhance the understanding of social cohesion and attitudes towards immigration.