The Promise and Perils of Big Data and AI for Migration

A special issue of Social Sciences (ISSN 2076-0760). This special issue belongs to the section "International Migration".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 6656

Special Issue Editors


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Guest Editor
Department of Sociology and Communication, University of Salamanca, 37007 Salamanca, Spain
Interests: migration; social media; big data; hate speech; ICT adoption; journalism

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Guest Editor
Interface Demography, Department of Sociology, Vrije Universiteit Brussel, 1050 Ixelles, Belgium
Interests: migration; equal opportunities; gender; social and public policies; big data and AI for societal challenges; mixed methods

Special Issue Information

Dear Colleagues,

Migration and human mobility studies have experienced an unprecedent era of accessibility to data and availability of sophisticated methods to produce insights. Specifically, big data (BD) and artificial intelligence (AI) provide a wide range of empirical and theoretical inputs to understand how and why people move and what the consequences of this social event are. These new attempts to use BD and AI to understand human mobility and migration are promising and pioneering steps for migration research, but this combination is still in its infancy. Moreover, some of the benefits of these approaches are controversial given the limitations of the methods (i.e., interpretability or bias of the algorithms, etc.) and the legal/ethical concerns that are involved. 

This Special Issue covers a wide range of topics that investigate the advance of BD and AI as a means to better understand various aspects of migration. These topics within the migration framework include (but are not necessary limited to): harnessing BD for migration and human mobility; case studies utilizing different BD sources (mobile phone, internet based, social media, remote sensing, financial big data, etc.) and/or AI techniques; developing indicators, predictive analytics, and forecasting with BD and AI methodologies; improving gaps in migration statistics with BD and/or AI techniques; bias in AI; humanitarian use of BD and AI; conceptual and legal aspects; social consequences of the implications of BD and new technologies; challenges and mitigation strategies; AI-supported decision making; border management and biometrics; blockchain technologies and digital identities; and the politics of BD and AI.

Prof. Dr. Carlos Arcila Calderón
Dr. Tuba Bircan
Guest Editors

Manuscript Submission Information

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Keywords

  • migration
  • migrants
  • refugees
  • big data
  • artificial intelligence
  • computational social sciences
  • ethics

Published Papers (2 papers)

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Research

16 pages, 2113 KiB  
Article
Online Hate Speech and Immigration Acceptance: A Study of Spanish Provinces
by Patricia Sánchez-Holgado, Javier J. Amores and David Blanco-Herrero
Soc. Sci. 2022, 11(11), 515; https://doi.org/10.3390/socsci11110515 - 14 Nov 2022
Cited by 5 | Viewed by 2478
Abstract
Online hate speech against migrants and refugees poses a grave challenge for coexistence and democracy. However, it also offers an opportunity to measure social acceptance towards this group. Using the Intergroup Contact and the Mediated Intergroup Contact Theory, and an already validated methodology, [...] Read more.
Online hate speech against migrants and refugees poses a grave challenge for coexistence and democracy. However, it also offers an opportunity to measure social acceptance towards this group. Using the Intergroup Contact and the Mediated Intergroup Contact Theory, and an already validated methodology, this article seeks to validate whether the use of hate speech as a predictor of social acceptance is valid at a provincial level in Spain. Contrasting 97,710 tweets about migrants and refugees with secondary data from public Spanish institutions about acceptance of immigration and foreign population, no correlation was observed, rejecting the main hypotheses, and hinting that the application of this approach might not be recommended for smaller entities, such as provinces (NUTS 3). However, the study offers descriptive data about racist hate speech spread on Twitter in Spain, and also discusses the need for more studies using big data to increase knowledge about online hate speech against migrants and refugees. Full article
(This article belongs to the Special Issue The Promise and Perils of Big Data and AI for Migration)
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14 pages, 319 KiB  
Article
Using Social Media to Monitor Conflict-Related Migration: A Review of Implications for A.I. Forecasting
by Hamid Akin Unver
Soc. Sci. 2022, 11(9), 395; https://doi.org/10.3390/socsci11090395 - 1 Sep 2022
Cited by 4 | Viewed by 3005
Abstract
Following the large-scale 2015–2016 migration crisis that shook Europe, deploying big data and social media harvesting methods became gradually popular in mass forced migration monitoring. These methods have focused on producing ‘real-time’ inferences and predictions on individual and social behavioral, preferential, and cognitive [...] Read more.
Following the large-scale 2015–2016 migration crisis that shook Europe, deploying big data and social media harvesting methods became gradually popular in mass forced migration monitoring. These methods have focused on producing ‘real-time’ inferences and predictions on individual and social behavioral, preferential, and cognitive patterns of human mobility. Although the volume of such data has improved rapidly due to social media and remote sensing technologies, they have also produced biased, flawed, or otherwise invasive results that made migrants’ lives more difficult in transit. This review article explores the recent debate on the use of social media data to train machine learning classifiers and modify thresholds to help algorithmic systems monitor and predict violence and forced migration. Ultimately, it identifies and dissects five prevalent explanations in the literature on limitations for the use of such data for A.I. forecasting, namely ‘policy-engineering mismatch’, ‘accessibility/comprehensibility’, ‘legal/legislative legitimacy’, ‘poor data cleaning’, and ‘difficulty of troubleshooting’. From this review, the article suggests anonymization, distributed responsibility, and ‘right to reasonable inferences’ debates as potential solutions and next research steps to remedy these problems. Full article
(This article belongs to the Special Issue The Promise and Perils of Big Data and AI for Migration)
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