Migration

Sustainable Development Goal 10

Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well- managed migration policies

ANTI-IMMIGRATION FACEBOOK AD TARGETING

“Clandestino or Rifugiato? Anti-immigration Facebook Ad Targeting in Italy” ACM CHI Conference on Human Factors in Computing Systems. 2021. Best Paper Award

Capozzi A, De Francisci Morales G, Mejova Y, Monti C, Panisson A, Paolotti D

Monitoring advertising around controversial issues is an important step in ensuring accountability and transparency of political processes. Here, we use the Facebook Ads Library to collect 2,312 migration-related advertising campaigns in Italy over one year. Our pro- and anti-immigration classifier reveals a partisan divide among the major Italian political parties, with anti-immigration ads accounting for nearly 15M impressions. We estimate that about two thirds of all captured campaigns use some kind of demographic targeting by location, gender, or age. Our study has policy implications for political communication: since the Facebook Ads Library does not allow to distinguish between advertisers intentions and algorithmic targeting, we argue that more details should be shared by platforms regarding the targeting configuration of socio-political campaigns.

NEWS AND INTERNAL DISPLACEMENT

“Developing Annotated Resources for Internal Displacement Monitoring” .

WWW '21: Companion Proceedings of the Web Conference 2021

Poletto F, Zhang Y, Panisson A, Mejova Y, Paolotti D, Ponserre S

In this study we designed and developed a novel annotation framework and of annotated resources for Internal Displacement, as the outcome of a collaboration with the Internal Displacement Monitoring Centre, aimed at improving the accuracy of their monitoring platform IDETECT. The schema includes multi-faceted description of the events, including cause, quantity of people displaced, location and date. Higher-order facets aimed at improving the information extraction, such as document relevance and type, are proposed. We also report a case study of machine learning application to the document classification tasks. Finally, we discuss the importance of standardized schema in dataset benchmark development and its impact on the development of reliable disaster monitoring infrastructure.

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