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

Title	Using social media to monitor conflict-related migration: A review of implications for A.I. forecasting
Author	Ünver, Hamid Akın
Publication Date:	2022-09
Publication Place	- MDPI
Subject	Artificial intelligence, Big data ethics, Conflict, Event data, Forced migration
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2076-0760
Record ID	fe42646b-9909-4c2c-bcba-db16099c461c
Library Location	International Relations
Date	2022-09
Notes	Science Academy Society of Turkey ; TÜBİTAK
Sample Text	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

Author Ünver, Hamid Akın

Publication Date 2022-09

Publication Place - MDPI

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

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2076-0760

Record ID fe42646b-9909-4c2c-bcba-db16099c461c

Library Location International Relations

Date 2022-09

Notes Science Academy Society of Turkey ; TÜBİTAK

Sample Text 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