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On microtargeting socially divisive ads: A case study of Russia-linked Ad campaigns on Facebook

  • Filipe N. Ribeiro
  • , Lucas Henrique
  • , Oana Goga
  • , Koustuv Saha
  • , Johnnatan Messias
  • , Krishna P. Gummadi
  • , Mahmoudreza Babaei
  • , Fabricio Benevenuto
  • , Elissa M. Redmiles
  • Universidade Federal de Ouro Preto
  • UFMG
  • University Grenoble Alpes
  • College of Computing
  • Max Planck Institute for Software Systems
  • University of Maryland, College Park

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Résumé

Targeted advertising is meant to improve the efficiency of matching advertisers to their customers. However, targeted advertising can also be abused by malicious advertisers to efficiently reach people susceptible to false stories, stoke grievances, and incite social conflict. Since targeted ads are not seen by non-targeted and non-vulnerable people, malicious ads are likely to go unreported and their effects undetected. This work examines a specific case of malicious advertising, exploring the extent to which political ads 1 from the Russian Intelligence Research Agency (IRA) run prior to 2016 U.S. elections exploited Facebook's targeted advertising infrastructure to efficiently target ads on divisive or polarizing topics (e.g., immigration, race-based policing) at vulnerable sub-populations. In particular, we do the following: (a) We conduct U.S. census-representative surveys to characterize how users with different political ideologies report, approve, and perceive truth in the content of the IRA ads. Our surveys show that many ads are “divisive”: they elicit very different reactions from people belonging to different socially salient groups. (b) We characterize how these divisive ads are targeted to sub-populations that feel particularly aggrieved by the status quo. Our findings support existing calls for greater transparency of content and targeting of political ads. (c) We particularly focus on how the Facebook ad API facilitates such targeting. We show how the enormous amount of personal data Facebook aggregates about users and makes available to advertisers enables such malicious targeting.

langue originaleAnglais
titreFAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency
EditeurAssociation for Computing Machinery, Inc
Pages140-149
Nombre de pages10
ISBN (Electronique)9781450361255
Les DOIs
étatPublié - 29 janv. 2019
Modification externeOui
Evénement2019 ACM Conference on Fairness, Accountability, and Transparency, FAT* 2019 - Atlanta, États-Unis
Durée: 29 janv. 201931 janv. 2019

Série de publications

NomFAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency

Une conférence

Une conférence2019 ACM Conference on Fairness, Accountability, and Transparency, FAT* 2019
Pays/TerritoireÉtats-Unis
La villeAtlanta
période29/01/1931/01/19

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

  1. SDG 10 - Inégalités réduites
    SDG 10 Inégalités réduites

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