October 2, 2022

New Study Analyzing Political Advertising on Facebook, Google, and TikTok

By Orestis Papakyriakopoulos, Christelle Tessono, Arvind Narayanan, Mihir Kshirsagar

With the 2022 midterm elections in the United States fast approaching, political campaigns are poised to spend heavily to influence prospective voters through digital advertising. Online platforms such as Facebook, Google, and TikTok will play an important role in distributing that content. But our new study – How Algorithms Shape the Distribution of Political Advertising: Case Studies of Facebook, Google, and TikTok — that will appear in the Artificial Intelligence, Ethics, and Society conference in August, shows that the platforms’ tools for voluntary disclosures about political ads do not provide the necessary transparency the public needs. More details can also be found on our website: campaigndisclosures.princeton.edu.

Our paper conducts the first large-scale analysis of public data from the 2020 presidential election cycle to critically evaluate how online platforms affect the distribution of political advertisements. We analyzed a dataset containing over 800,000 ads about the 2020 U.S. presidential election that ran in the 2 months prior to the election, which we obtained from the ad libraries of Facebook and Google that were created by the companies to offer more transparency about political ads. We also collected and analyzed 2.5 million TikTok videos from the same time period. These ad libraries were created by the platforms in an attempt to stave off potential regulation such as the Honest Ads Act, which sought to impose greater transparency requirements for platforms carrying political ads. But our study shows that these ad libraries fall woefully short of their own objectives to be more transparent about who pays for the ads and who sees the ads, as well the objectives of bringing greater transparency about the role of online platforms in shaping the distribution of political advertising. 

We developed a three-part evaluative framework to assess the platform disclosures: 

1. Do the disclosures meet the platforms’ self-described objective of making political advertisers accountable?

2. How do the platforms’ disclosures compare against what the law requires for radio and television broadcasters?

3. Do the platforms disclose all that they know about the ad targeting criteria, the audience for the ads, and how their algorithms distribute or moderate content?

Our analysis shows that the ad libraries do not meet any of the objectives. First, the ad libraries only have partial disclosures of audience characteristics and targeting parameters of placed political ads. But these disclosures do not allow us to understand how political advertisers reached prospective voters. For example, we compared ads in the ad libraries that were shown to different audiences with dummy ads that we created on the platforms (Figure 1). In many cases, we measured a significant difference between the calculated cost-per-impression between the two types of ads, which we could not explain with the available data.

  • Figure 1. We plot the generated cost per impression of ads in the ad-libraries that were (1) targeted to all genders & ages on Google, (2) to Females, between 25-34 on YouTube, (3) were seen by all genders & ages in the US on Facebook, and (4) only by females of all ages located in California on Facebook.  For Facebook, lower & upper bounds are provided for the impressions. For Google, lower & upper bounds are provided for cost & impressions, given the extensive “bucketing” of the parameters performed by the ad libraries when reporting them, which are denoted in the figures with boxes. Points represent the median value of the boxes. We compare the generated cost-per impression of ads with the cost-per impression of a set of dummy ads we placed on the platforms with the exact same targeting parameters & audience characteristics. Black lines represent the upper and lower boundaries of an ad’s cost-per-impression as we extracted them from the dummy ads. We label an ad placement as “plausible targeting”, when the ad cost-per-impression overlaps with the one we calculated, denoting that we can assume that the ad library provides all relevant targeting parameters/audience characteristics about an ad.  Similarly, an placement labeled as `”unexplainable targeting’”  represents an ad whose cost-per-impression is outside the upper and lower reach values that we calculated, meaning that potentially platforms do not disclose full information about the distribution of the ad.

Second, broadcasters are required to offer advertising space at the same price to political advertisers as they do to commercial advertisers. But we find that the platforms charged campaigns different prices for distributing ads. For example, on average, the Trump campaign on Facebook paid more per impression (~18 impressions/dollar) compared to the Biden campaign (~27 impressions/dollar). On Google, the Biden campaign paid more per impression compared to the Trump campaign. Unfortunately, while we attempted to control for factors that might account for different prices for different audiences, the data does not allow us to probe the precise reason for the differential pricing. 

Third, the platforms do not disclose the detailed information about the audience characteristics that they make available to advertisers. They also do not explain how the algorithms distribute or moderate the ads. For example, we see that campaigns placed ads on Facebook that were not ostensibly targeted by age, but the ad was not distributed uniformly.  We also find that platforms applied their ad moderation policies inconsistently, with some instances of moderated ads being removed and some others not, and without any explanation for the decision to remove an ad. (Figure 2) 

  • Figure 2. Comparison of different instances of moderated ads across platforms. The light blue bars show how many instances of a single ad were moderated, and maroon bars show how many instances of the same ad were not. Results suggests an inconsistent moderation of content across platforms, with some instances of the same ad being removed and some others not.

Finally, we observed new forms of political advertising that are not captured in the ad libraries. Specifically, campaigns appear to have used influencers to promote their messages without adequate disclosure. For example, on TikTok, we document how political influencers, who were often linked with PACs, generated billions of impressions from their political content. This new type of campaigning still remains unregulated and little is known about the practices and relations between influencers and political campaigns.  

In short, the online platform self-regulatory disclosures are inadequate and we need more comprehensive disclosures from platforms to understand their role in the political process. Our key recommendations include:

– Requiring that each political entity registered with the FEC use a single, universal identifier for campaign spending across platforms to allow the public to track their activity.

– Developing a cross-platform data repository, hosted and maintained by a government or independent entity, that collects political ads, their targeting criteria, and the audience characteristics that received them. 

– Requiring platforms to disclose information that will allow the public to understand how the algorithms distribute content and how platforms price the distribution of political ads. 

– Developing a comprehensive definition of political advertising that includes influencers and other forms of paid promotional activity.

The Silver Effect: What We Can Learn from Poll Aggregators

For those who now think Nate Silver is god, here’s a question: Can Nate Silver make a prediction so accurate that Nate Silver himself doesn’t believe it?

Yes, he can–and he did. Silver famously predicted the results of Election 2012 correctly in every state. Yet while his per-state predictions added up to the 332 electoral votes that Obama won, Silver himself predicted that Obama’s expected electoral vote total was only 313. Why? Because Silver predicted that Silver would get some states wrong. Unpacking this (pseudo-)paradox can help us understand what we can and can’t learn from the performance of poll aggregators like Nate Silver and Princeton’s Sam Wang in this election.
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Voting technology issues in Virginia on election day

I spent Election Day in one of the command centers for the 866-OUR-VOTE hotline. The command center was accepting calls from New Jersey, Maryland, DC, and Virginia, but 95% of the technology issues were from Virginia. I was the designated “technology guy”, so pretty much everything that came through that center came to me. This gave me a pretty good perspective on the scope of issues. (I don’t know about the non-technology issues, although I heard discussions of issues like demanding more ID than is required, voter intimidation, etc.)

Following is a summary of what I saw. What’s most interesting is that if you divide things into “easy to solve” and “hard to solve”, the “easy to solve” ones are all in places using optical scan, and the “hard to solve” are all in places using DREs (colloquially known as “touch screens”, although not all of them are).
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Get Out the Vote, Cee-Lo Style?

This semester, Ed Felten and I are teaching a Freshman Seminar called “Facebook: The Social Impact of Social Networks.” This week, the class is discussing a recent article published in the journal Nature, entitled “A 61-Million-Person Experiment in Social Influence and Political Mobilization“. The study reveals that if Facebook shows you a list of your closest friends who have voted, you are more likely to do so yourself. It is a fascinating read both because it is probably the first very-large-scale controlled test of social influence via online social networks, and because it appears that without much work the company was able to spur about 340,000 extra people to vote in the 2010 midterm elections.

I confess that last night I watched some of the wildly popular reality TV competition The Voice. What can I say? The pyrotechnics were more calming than the amped-up CNN spin-zoners. It was the first day that the at-home audience began voting for their favorites. Carson Daly mentioned that the show would take the requisite break on Election Night, but return in force on Wednesday. (Incidentally, I can’t decide whether or not this video urging us to “vote Team Cee-Lo” is too clever by half).
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Grading the absentee-in-person experience in Virginia

[Each year, I write a “my day as a pollworker” report. This year, I’m not a pollworker, or election officer in Virginia parlance, for a variety of reasons, so I decided to write about my voting experience.]

I just got back from “in-person absentee voting”. This is similar to but not the same as early voting – in Virginia, it’s still absentee voting, but you do it by going to a central polling place (there are almost a dozen in Fairfax, which is a very geographically large and populous county). And you have to have one of a dozen reasons (e.g., you’ll be out of the county on business or pleasure, you’re disabled, pregnant, incarcerated awaiting trial, …) – you can’t just do it because it’s more convenient. See Code of Virginia 24.2-700 for all of the acceptable reasons.

My goal, besides the actual act of voting, was twofold. First, Virginia has new voter ID laws, and I wanted to see whether pollworkers had been trained to know what the new laws are. And second, Fairfax County by policy is supposed to offer voters the choice of “paper or plastic” – optical scan or DRE, and I wanted to see how that happened. (I know how it has happened in the past in my precinct, because I was responsible for ensuring that we followed the rules, but wanted to see how it was done in this environment.)
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