Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration
Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration
复制标题
将嘈杂的社交媒体信号与传统的运动变量相结合来预测被迫迁移
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Kornraphop Kawintiranon
中科院分区：
文献类型：
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作者：
L. Singh;Laila Wahedi;Yanchen Wang;Yifang Wei;Christo Kirov;Susan F. Martin;K. Donato;Yaguang Liu;Kornraphop Kawintiranon
世界范围内由于战争和冲突造成的流离失所达到历史最高水平。不幸的是，决定人们是否、何时、何地迁移是一个复杂的问题。本文建议将来自社交媒体和报纸的公开有机数据与更传统的被迫迁移指标结合起来，以确定人们将在何时何地迁移。我们将运动和有机变量与不同贝叶斯模型中的空间和时间变化结合起来，并通过一个涉及伊拉克流离失所的案例研究显示了我们方法的可行性。我们的分析表明，与传统变量相比，合并开源生成的会话和事件变量保持或提高了预测的准确性。这项工作是理解如何利用有机大数据解决社会规模问题的重要一步。
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.
移民理论中缺失的元素。
DOI：
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发表时间：
2015
期刊：
Migration letters : an international journal of migration studies
影响因子：
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作者：
Massey,DouglasS
通讯作者：
Massey,DouglasS