@inproceedings{5bff4ef64e7c4df0adf197c07af1ca20,
title = "On microtargeting socially divisive ads: A case study of Russia-linked Ad campaigns on Facebook",
abstract = " 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.",
keywords = "Advertisements, News media, Perception bias, Social divisiveness, Social media, Targeting",
author = "Ribeiro, {Filipe N.} and Lucas Henrique and Oana Goga and Koustuv Saha and Johnnatan Messias and Gummadi, {Krishna P.} and Mahmoudreza Babaei and Fabricio Benevenuto and Redmiles, {Elissa M.}",
note = "Funding Information: F. Benvenuto and F. Ribeiro acknowledge grants from Capes, CNPq, and Fapemig. E. M. Redmiles acknowledges support from the U.S. National Science Foundation Graduate Research Fellowship Program under Grant No. DGE 1322106 and from a Facebook Fellowship. This research was partly supported by an European Research Council (ERC) Advanced Grant for the project “Foundations for Fair Social Computing”, funded under the European Union's Horizon 2020 Framework Programme (grant agreement no. 789373). This research was partly supported by ANR through the grant ANR-17-CE23-0014. Funding Information: F. Benvenuto and F. Ribeiro acknowledge grants from Capes, CNPq, and Fapemig. E. M. Redmiles acknowledges support from the U.S. National Science Foundation Graduate Research Fellowship Program under Grant No. DGE 1322106 and from a Facebook Fellowship. This research was partly supported by an European Research Council (ERC) Advanced Grant for the project “Foundations for Fair Social Computing”, funded under the European Union{\textquoteright}s Horizon 2020 Framework Programme (grant agreement no. 789373). This research was partly supported by ANR through the grant ANR-17-CE23-0014. Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 2019 ACM Conference on Fairness, Accountability, and Transparency, FAT* 2019 ; Conference date: 29-01-2019 Through 31-01-2019",
year = "2019",
month = jan,
day = "29",
doi = "10.1145/3287560.3287580",
language = "English (US)",
series = "FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency",
publisher = "Association for Computing Machinery",
pages = "140--149",
booktitle = "FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency",
address = "United States",
}