Fatehkia Masoomali, Del Villar Zinnya, Koebe Till, Letouzé Emmanuel, Lozano Andres, Al Feel Roaa, Mrad Fouad, Weber Ingmar
Qatar Computing Research Institute, HBKU, Doha, Qatar.
Data-Pop Alliance, New York City, NY, United States.
Front Big Data. 2022 Nov 30;5:1033530. doi: 10.3389/fdata.2022.1033530. eCollection 2022.
While the fighting in the Syrian civil war has mostly stopped, an estimated 5.6 million Syrians remain living in neighboring countries. Of these, an estimated 1.5 million are sheltering in Lebanon. Ongoing efforts by organizations such as UNHCR to support the refugee population are often ineffective in reaching those most in need. According to UNHCR's 2019 Vulnerability Assessment of Syrian Refugees Report (VASyR), only 44% of the Syrian refugee families eligible for multipurpose cash assistance were provided with help, as the others were not captured in the data. In this project, we are investigating the use of non-traditional data, derived from Facebook advertising data, for population level vulnerability assessment. In a nutshell, Facebook provides advertisers with an estimate of how many of its users match certain targeting criteria, e.g., how many Facebook users currently living in Beirut are "living abroad," aged 18-34, speak Arabic, and primarily use an iOS device. We evaluate the use of such audience estimates to describe the spatial variation in the socioeconomic situation of Syrian refugees across Lebanon. Using data from VASyR as ground truth, we find that iOS device usage explains 90% of the out-of-sample variance in poverty across the Lebanese governorates. However, evaluating predictions at a smaller spatial resolution also indicate limits related to sparsity, as Facebook, for privacy reasons, does not provide audience estimates for fewer than 1,000 users. Furthermore, comparing the population distribution by age and gender of Facebook users with that of the Syrian refugees from VASyR suggests an under-representation of Syrian women on the social media platform. This work adds to growing body of literature demonstrating the value of anonymous and aggregate Facebook advertising data for analysing large-scale humanitarian crises and migration events.
虽然叙利亚内战中的战斗大多已经停止,但估计仍有560万叙利亚人生活在邻国。其中,估计有150万人在黎巴嫩避难。联合国难民署等组织为支持难民群体所做的持续努力,在帮助最有需要的人方面往往成效不佳。根据联合国难民署2019年叙利亚难民脆弱性评估报告(VASyR),只有44%符合多用途现金援助条件的叙利亚难民家庭得到了帮助,因为其他家庭未被数据记录。在这个项目中,我们正在研究如何利用源自脸书广告数据的非传统数据进行人口层面的脆弱性评估。简而言之,脸书会为广告商提供其用户中符合某些目标标准的人数估计,例如,目前居住在贝鲁特、“生活在国外”、年龄在18至34岁之间、说阿拉伯语且主要使用苹果iOS设备的脸书用户有多少。我们评估利用此类受众估计来描述黎巴嫩各地叙利亚难民社会经济状况的空间差异。以VASyR的数据作为基本事实,我们发现iOS设备的使用情况可以解释黎巴嫩各省样本外贫困差异的90%。然而,在较小空间分辨率下评估预测结果也显示出与数据稀疏性相关的局限性,因为出于隐私原因,脸书不会为少于1000名用户提供受众估计。此外,将脸书用户的年龄和性别分布与VASyR中的叙利亚难民进行比较,结果表明叙利亚女性在这个社交媒体平台上的代表性不足。这项工作为越来越多的文献增添了内容,这些文献证明了匿名和汇总的脸书广告数据在分析大规模人道主义危机和移民事件方面的价值。