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Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration

Authors:

Katharine Donato,

Kornraphop KawintiranonAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 1975 - 1983

https://doi.org/10.1145/3292500.3330774

Published: 25 July 2019 Publication History

Abstract

Worldwide displacement due to war and conflict is at all-time high. Unfortunately, determining if, when, and where people will move is a complex problem. This paper proposes integrating both publicly available organic data from social media and newspapers with more traditional indicators of forced migration to determine when and where people will move. We combine movement and organic variables with spatial and temporal variation within different Bayesian models and show the viability of our method using a case study involving displacement in Iraq. Our analysis shows that incorporating open-source generated conversation and event variables maintains or improves predictive accuracy over traditional variables alone. This work is an important step toward understanding how to leverage organic big data for societal--scale problems.

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Cited By

Wycoff NSingh LArab ADonato KMarahrens H(2024)The digital trail of Ukraine’s 2022 refugee exodusJournal of Computational Social Science10.1007/s42001-024-00304-47:2(2147-2193)Online publication date: 16-Jul-2024
https://doi.org/10.1007/s42001-024-00304-4
Wycoff NArab ADonato KSingh LKawintiranon KLiu YJacobs E(2023)Forecasting Ukrainian Refugee Flows With Organic Data SourcesInternational Migration Review10.1177/01979183231203931Online publication date: 9-Oct-2023
https://doi.org/10.1177/01979183231203931
Henningsen G(2023)Big Data for the Prediction of Forced DisplacementInternational Migration Review10.1177/01979183231195296Online publication date: 23-Aug-2023
https://doi.org/10.1177/01979183231195296
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Index Terms

Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social engineering (social sciences)
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
KenSci
,
Vipin Kumar
University of Minnesota
,
Program Chairs:
Ying Li
EV Analysis Corporation
,
Rómer Rosales
LinkedIn
,
Evimaria Terzi
Boston University
,
George Karypis
University of Minnesota

Copyright © 2019 ACM.

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Published: 25 July 2019

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MacArthur Foundation
Canadian Social Science and Humanities Research Council
National Science Foundation

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KDD '19

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KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 4 - 8, 2019

AK, Anchorage, USA

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KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Wycoff NSingh LArab ADonato KMarahrens H(2024)The digital trail of Ukraine’s 2022 refugee exodusJournal of Computational Social Science10.1007/s42001-024-00304-47:2(2147-2193)Online publication date: 16-Jul-2024
https://doi.org/10.1007/s42001-024-00304-4
Wycoff NArab ADonato KSingh LKawintiranon KLiu YJacobs E(2023)Forecasting Ukrainian Refugee Flows With Organic Data SourcesInternational Migration Review10.1177/01979183231203931Online publication date: 9-Oct-2023
https://doi.org/10.1177/01979183231203931
Henningsen G(2023)Big Data for the Prediction of Forced DisplacementInternational Migration Review10.1177/01979183231195296Online publication date: 23-Aug-2023
https://doi.org/10.1177/01979183231195296
Agrawal ASingh LJacobs ELiu YDunlevy GPokharel RUppala V(2023)All Translation Tools Are Not Equal: Investigating the Quality of Language Translation for Forced Migration2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302481(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302481
Domson OPadilla JSong GFrydenlund E(2023)A Bayesian Approach of Predicting the Movement of Internally Displaced PersonsSocial, Cultural, and Behavioral Modeling10.1007/978-3-031-43129-6_24(241-250)Online publication date: 16-Sep-2023
https://doi.org/10.1007/978-3-031-43129-6_24
Martin SSingh L(2022)Environmental change and human mobility: Opportunities and challenges of big dataInternational Migration10.1111/imig.1300261:5(29-46)Online publication date: 10-Apr-2022
https://doi.org/10.1111/imig.13002
Hoffmann Pham KLuengo-Oroz M(2022)Predictive modelling of movements of refugees and internally displaced people: towards a computational frameworkJournal of Ethnic and Migration Studies10.1080/1369183X.2022.210054649:2(408-444)Online publication date: 16-Aug-2022
https://doi.org/10.1080/1369183X.2022.2100546
Tai XMehra SBlumenstock J(2022)Mobile phone data reveal the effects of violence on internal displacement in AfghanistanNature Human Behaviour10.1038/s41562-022-01336-46:5(624-634)Online publication date: 12-May-2022
https://doi.org/10.1038/s41562-022-01336-4
Blasi Casagran CBoland CSánchez-Montijano EVilà Sanchez E(2021)The Role of Emerging Predictive IT Tools in Effective Migration GovernancePolitics and Governance10.17645/pag.v9i4.44369:4(133-145)Online publication date: 28-Oct-2021
https://doi.org/10.17645/pag.v9i4.4436
Singh LPadden CDavis-Kean PDavid RMarwadi VRen YVanarsdall R(2021)Text Analytic Research Portals: Supporting Large-Scale Social Science Research2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671696(6020-6022)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671696
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