Online targeted ads are those shown only to certain users based on interests, demographics, or behaviors. Because targeted ads raise many privacy concerns, many platforms provide ad transparency systems (ATSs) to inform users about this practice. To better understand what current ATSs are communicating to users—and how—we first taxonomized the design and content of 22 of the most popular English-language websites' ATSs as presented to users in the United States. We found substantial differences across ATSs in both the prevalence of transparency-enhancing features (e.g., whether they show users what has been inferred about them) and the presentation of information (e.g., the terminology used, where settings are located). Across all platforms, however, we observed consistent ambiguity about what data is used to target ads and the actual impact of altering settings. To gauge how these different design choices impact users, we conducted an online user study in which 198 participants used their own account to explore the ATS of one of eight representative platforms. We found that many of the questions participants hoped the ATS would answer remained unanswered after exploring the ATS. More broadly, participants found current ATSs simultaneously complex and lacking key details. We pinpoint ATS design decisions that best support users.

In 2021, the U.S. Hispanic population totaled 62.5 million people, 68% of whom spoke Spanish in their homes. To date, it is unclear which political advertisers address this audience in their preferred language, and whether they do so differently than for English-speaking audiences. In this work, we study differences between political Facebook ads in English and Spanish during 2020, the latest U.S. presidential election. Political advertisers spent $ 1.48 B in English, but only $ 28.8 M in Spanish, disproportionately little compared to the share of Spanish speakers in the population. We further find a lower proportion of election-related advertisers (which additionally are more liberal-leaning than in the English set), and a higher proportion of government agencies in the set of Spanish ads. We perform multilingual topic classification, finding that the most common ad topics in English were also present in Spanish, but to a different extent, and with a different composition of advertisers. Thus, Spanish speakers are served different types of ads from different types of advertisers than English speakers, and in lower amounts; these results raise the question of whether political communication through Facebook ads may be inequitable and effectively disadvantaging the sizeable minority of Spanish speakers in the U.S. population.

Facebook has become an important platform for news publishers to promote their work and engage with their readers. Some news pages on Facebook have a reputation for consistently low factualness in their reporting, and there is concern that Facebook allows their misinformation to reach large audiences. To date, there is remarkably little empirical data about how often users "like," comment and share content from news pages on Facebook, how user engagement compares between sources that have a reputation for misinformation and those that do not, and how the political leaning of the source impacts the equation. In this work, we propose a methodology to generate a list of news publishers' official Facebook pages annotated with their partisanship and (mis)information status based on third-party evaluations, and collect engagement data for the 7.5 M posts that 2,551 U.S. news publishers made on their pages during the 2020 U.S. presidential election. We propose three metrics to study engagement (1) across the Facebook news ecosystem, (2) between (mis)information providers and their audiences, and (3) with individual pieces of content from (mis)information providers. Our results show that misinformation news sources receive widespread engagement on Facebook, accounting for 68.1% of all engagement with far-right news providers, followed by 37.7 % on the far left. Individual posts from misinformation news providers receive consistently higher median engagement than non-misinformation in every partisanship group. While most prevalent on the far right, misinformation appears to be an issue across the political spectrum.

Actors engaged in election disinformation are using online advertising platforms to spread political messages. In response to this threat, online advertising networks have started making political advertising on their platforms more transparent in order to enable third parties to detect malicious advertisers. We present a set of methodologies and perform a security analysis of Facebook's U.S. Ad Library, which is their political advertising transparency product. Unfortunately, we find that there are several weaknesses that enable a malicious advertiser to avoid accurate disclosure of their political ads. We also propose a clustering-based method to detect advertisers engaged in undeclared coordinated activity. Our clustering method identified 16 clusters of likely inauthentic communities that spent a total of over four million dollars on political advertising. This supports the idea that transparency could be a promising tool for combating disinformation. Finally, based on our findings, we make recommendations for improving the security of advertising transparency on Facebook and other platforms.