Using social media to monitor conflict-related migration: A review of implications for A.I. forecasting

Name: Using social media to monitor conflict-related migration: A review of implications for A.I. forecasting
Author: Ünver, Hamid Akın

İsim	Using social media to monitor conflict-related migration: A review of implications for A.I. forecasting
Yazar	Ünver, Hamid Akın
Basım Tarihi:	2022-09
Basım Yeri	- MDPI
Konu	Artificial intelligence, Big data ethics, Conflict, Event data, Forced migration
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2076-0760
Kayıt Numarası	fe42646b-9909-4c2c-bcba-db16099c461c
Lokasyon	International Relations
Tarih	2022-09
Notlar	Science Academy Society of Turkey ; TÜBİTAK
Örnek Metin	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.
DOI	10.3390/socsci11090395
Cilt	11

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Using social media to monitor conflict-related migration: A review of implications for A.I. forecasting

Yazar Ünver, Hamid Akın

Basım Tarihi 2022-09

Basım Yeri - MDPI

Konu Artificial intelligence, Big data ethics, Conflict, Event data, Forced migration

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2076-0760

Kayıt Numarası fe42646b-9909-4c2c-bcba-db16099c461c

Lokasyon International Relations

Tarih 2022-09

Notlar Science Academy Society of Turkey ; TÜBİTAK

Örnek Metin 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.

DOI 10.3390/socsci11090395

Cilt 11