September 26, 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:

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.

Will Web3 Follow in the Footsteps of the AI Hype Cycle?

For many, the global financial crisis of 2008 marked a turning point for trust in established institutions. It is unsurprising that during this same historical time period, Bitcoin, a decentralized cryptocurrency that aspired to operate independent from state manipulation, began gaining traction. Since the birth of Bitcoin, other decentralized technologies have been introduced that enable a broader range of functionalities including decentralized finance (DeFi), non-fungible tokens (NFTs), a wide range of other cryptocurrencies, and decentralized autonomous organizations (DAOs). 

These types of technologies constitute what is sometimes referred to as “web3.” In contrast to web2, our current version of the web, which relies heavily on centralized platforms and corporate intermediaries–think Facebook’s social network or Amazon’s webshop–web3 promises to redistribute power and agency back into the hands of users through decentralized peer-to-peer technology. Although web3 has garnered fervent support and equally fervent critique, it is undeniable that cryptocurrencies and other decentralized technologies have captured the mainstream imagination. 

What is less clear is whether the goals and practices of emerging businesses in the web3 sector align with, or stand in conflict with, the ideologies of web3’s most enthusiastic supporters. Organizational sociology has long established that organizations’ external rhetoric, which is shaped by a field’s perception of what is culturally and socially legitimate, may not fully align with their internal rhetoric or day-to-day practices. Continuing in this tradition, in a recent study, my colleague at Princeton’s Center for Information Technology Policy, researcher Elizabeth Watkins, and I sought to understand how people working at artificial intelligence (AI) startups think about, build, and publicly discuss their technology. We conducted interviews with 23 individuals working at early-stage AI startups across a variety of industry domains including healthcare, agriculture, business intelligence, and others. We asked them about how their AI works as well as about the pressures they face as they try to grow their companies.

In our interviews, the most prevalent theme we observed was that startup founders and employees felt they needed to hype up their AI to potential investors and clients. Widespread narratives about the transformative potential of AI have led non-AI savvy stakeholders to have unrealistic expectations about what AI can do– expectations that AI startups must contend with to gain market adoption. Some, for instance, have resorted to presenting artificially inflated estimates of their models’ performance to satisfy the demands of investors or clients that don’t really understand how models work or how they should be evaluated. From the perspective of the startup entrepreneurs we interviewed, if other AI startups promise the moon, it is difficult for their companies to compete if all they promise is a moon-shaped rock, especially if potential clients and investors cannot tell the difference. At the same time, these startup entrepreneurs did not actually buy into the hype themselves. Afterall, as AI practitioners, they know as well as any other tech skeptic what the limitations of AI are. 

In our AI startups study, several participants likened the hype surrounding AI to the hype that also surrounds blockchain, the backbone that undergirds decentralized technology. Yet unlike AI companies who hope to disrupt existing modes of performing tasks, hardline web3 evangelists see decentralized technology as a mechanism for disrupting the existing social, political, and economic order. That kind of disruption would take place on an entirely different scale than AI companies attempting to make tedious or boring tasks a little more automatic. But are web3 businesses actually hoping to effect the same kind of wide sweeping societal change web3 evangelists are hoping for?

In a study I’m kicking off with Johannes Lenhard, an anthropologist at the University of Cambridge who studies venture capital investors, we aim to understand where the ideological rubber of web3 meets the often unforgiving road to commercial success. We will interview entrepreneurs working at web3 businesses and investors working at investment firms with a focus on web3. Through these interviews, we aim to understand what their ideological visions of web3 are and the extent to which they have been able to realize those visions into real-world technology and business practices. 

As a preliminary glimpse into these questions, I did a quick and dirty analysis* of content from the blogs that Andreessen Horowitz (a16z), a prominent venture capital firm, posted about the companies in their web3 portfolio (top image). In order to get insight into the rhetoric of the companies themselves, I also looked at content from the landing pages of several of a16z’s web3 portfolio companies (bottom image). Visualization of the most frequently used terms of both data sources are below where bigger words are those that are used more frequently.

Word cloud from a16z’s blog posts

Word cloud from portfolio companies’ landing pages

Although this analysis is by no means scientific, it suggests that whereas companies’ external rhetoric emphasizes technical components, investors’ external rhetoric emphasizes vision. 

We don’t yet know whether we will observe these kinds of trends in our new study, but we hope to gain deeper empirical insights into both the public facing discourse of web3 stakeholder groups as well as into the rhetoric they use internally to shape their own self-perception and practices. Will blockchain shepherd in a newer, more democratic version of the web? A borderless society? Decentralized governance by algorithms? Or will it instead deliver only a few interesting widgets and business as usual? We’ll report back when we find out!

Interested in hearing more about the study or participating? Send me an email at .

*analysis performed on March 9th, 2022

Recommendations for introducing greater safeguards and transparency into CS conference funding

In Part 1 of this piece, I provided evidence of the extent to which some of the world’s top computer science conferences are financially reliant upon some of the world’s most powerful technology companies. In this second part, I lay out a set of recommendations for ways to help ensure that these entanglements of industry and academia don’t grant companies undue influence over the conditions of knowledge creation and exchange.

To be clear, I am not suggesting that conferences stop accepting money from tech companies, nor am I saying there is no place for Big Tech investment in academic research. I am simply advocating for conference organizers to adopt greater safeguards to increase transparency and mitigate the potential agenda-setting effects associated with companies’ funding of and presence in academic spaces.

While I am not claiming that sponsors have any say over which papers are or aren’t published, in the next few paragraphs I will show how agenda-setting can happen in a much more subtle yet pervasive way.

Resurrecting conferences as “trading zones”

Setting the agenda in a given field means determining and prioritizing topics of focus and investment. Research priorities are not neutral or naturally occurring—they are the result of social and political construction. And because a great deal of CS funding comes from tech companies, these priorities are likely to be shaped by what is considered valuable or profitable to those companies.

An example of the tech industry’s agenda-setting power includes the way in which AI/ML research has been conceptualized in narrower terms to prioritize technical work. For instance, despite its valuable contributions to the understanding of priorities inherent in ML research, the Birhane et. al. paper I cited in Part 1 was rejected from publication at the 2021 NeurIPS Conference with a dismissive meta-review, which is just one example of how the ML community has marginalized critical work and elevated technical work. Other examples of corporate agenda-setting in CS include the aforementioned way in which tech companies’ definitions of privacy and security vary from those of consumer advocates, and the way in which the field of human-computer interaction (HCI) often focuses on influencing user behavior rather than stepping back to reflect on necessary systemic changes at the platform level.

In deciding which conferences to fund, and shaping which ideas and work get elevated within those conferences, tech companies contribute to the creation of a prestige hierarchy. This, in turn, influences which kinds of people who self-select into submitting their work to and attending those conferences. Further, the sponsorship perks afford companies a prominent presence at CS conferences through expos and other events. Combined, these factors mold CS conferences into sites of commercially oriented activity.

It is important to make space at top conferences for work that doesn’t necessarily advance commercial innovation. Beyond simply serving as a channel for publishing and broadcasting academic papers, conferences have the potential to serve as sites of critique, activism and advocacy. These seemingly secondary functions of academic gatherings are, in actuality, critical functions that need to be preserved.

In “Engaging, Designing and Making Digital Systems,” Janet Vertesi et al. describe spaces of collaboration between scholarship and design as “trading zones”, where engagements can be corporate, critical, inventive, or focused on inquiry. While corporate work engages from within companies, critical engagement requires the existence of a trading zone in which domain scientists, computer scientists and engineers can meet and engage in dialogue. Vertesi et al. write, “Critical engagements typically embrace intersections between IT research and corporations yet eschew immediate pay-offs for companies or designers.”

Even if sponsoring companies don’t have a direct hand in deciding which work gets published, their presence at academic conferences gives them both insight into ideas and work being shared among attendees, and opportunities to push specific messaging around their brand through advertising and recruitment events. Therefore, instituting sponsorship policies and increasing transparency would help to both curb their potential influence, as well as make clear to conference participants the terms of companies’ financial contributions.

Introducing greater safeguards around conference sponsorship would not be unprecedented; for example, there have been similar efforts in the medical community to curb the influence of pharmaceutical and medical device manufacturing companies on clinical conferences.

Asking accountability conferences to practice what they preach

In particular, tech conferences whose mission is explicitly related to ethics and accountability deserve a higher level of scrutiny for their donor relationships. However, my survey of some of the most prominent conferences in this space found that many of them do not provide a list of donors, nor do they disclose any sponsorship policies on their websites.

That said, some conferences have been reevaluating their fundraising practices after recognizing that certain sponsors’ actions were not aligning with their values. For example, in March 2021, the ACM Conference for Fairness, Accountability, and Transparency (FAccT) suspended its sponsorship relationship with Google in protest of the company’s firing of two of its top Ethical AI researchers, who had been examining biases built into the company’s AI systems.

FAccT committee member Suresh Venkatasubramanian tweeted that the decision to drop Google as a supporter was “in the best interests of the community” while the committee revised its sponsorship policy. Conference sponsorship co-chair Michael Ekstrand told VentureBeat that having Google as a sponsor could impede FAccT’s Strategic Plan. (It should be noted that FAccT still accepted funding from DeepMind, a subsidiary of Google’s parent company Alphabet, for its 2021 conference.)

The conference recently published a new sponsorship policy, acknowledging that “outside contributions raise serious concerns about the independence of the conference and the legitimacy that the conference may confer on sponsors and supporters.” Other conferences, like the ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) and the Association for Computational Linguistics (ACL) Conference have also posted sponsorship and/or conflict of interest policies on their websites.

While it might be expected that ethics and fairness-oriented conferences would have a more robust protocol around which funds they accept, it is in the best interest of all CS conferences to think critically about and mitigate the constraints associated with accepting corporate sponsorship. 

Recommendation of Best Practices

In many instances, accepting corporate sponsorship is a necessary evil that enables valuable work to be done and allows greater access to resources and opportunities like conferences. In the long term, there should be a concerted effort to resurrect computer science conferences as a neutral territory for academic exploration based on what scholars, not corporations, deem to be worthy of pursuit. However, a more immediate solution could be to establish and enforce a series of best practices to ensure greater academic integrity of conferences that do rely on corporate sponsorship. 

Many scholars, like those who signed the Funding Matters petition in 2018, have argued in favor of establishing rigorous criteria and guidelines for corporate sponsorship of research conferences. I have developed a set of recommendations for conferences to serve as a jumping-off point for ensuring greater transparency and accountability in their decision-making process: 

  • Evaluate sponsors through the lens of your organization’s mission and values. Determine which lines you’re not willing to cross.
    • Are there companies whose objectives or outputs run counter to your values? Are there actions you refuse to legitimize or companies whose reputation might significantly compromise the integrity of the conferences they fund? Review your existing sponsors to ensure that none of them are crossing that line, and use it as a threshold for determining whether to accept funding from others in the future.
    • For example, in the sponsorship policy for the ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), organizers reserve the right to “decline or return any funding should the sponsorship committee decide that the funding source is misaligned with the mission of the initiative and conference.”
  • Be transparent about who is sponsoring your conference, how much they are contributing, and what benefits they receive as a condition of their contributions.
    • While many conferences do list the logos of their sponsors on their websites, it is not often clear how much money those organizations gave and how exactly it was used. To ensure greater transparency, publish a list of sponsors on your website and in other promotional materials and make the details of your call for sponsorship publicly available and easily accessible. 
    • Make sure to make this information public ahead of the conference, so that invited speakers and other attendees can make an informed decision about whether or not they want to participate. (1)
  • Develop rigorous policies to prevent sponsors from influencing the content or speakers of conference events. 
    • Establish a solid gift acceptance policy and thorough gift agreement outlining the kinds of funding you will and will not accept to ensure that your donors’ support is not restricted and does not come with strings attached.
    • For example, the FAccT conference recently published a new statement outlining their practices around sponsorship and financial support, which denies sponsors say over any part of the conference organization or content. In addition, sponsors can only contribute to a general fund, rather than being able to specify how their contributions are used.
  • Encourage open discussion during the conference about the implications of accepting corporate funding and potential alternatives.
    • For example, the ACM Conference on Computer Science and Law has committed to devoting time to a “discussion of practical strategies for and ethical implications of different funding models for both research and conference sponsorship in the nascent ACM Computer Science and Law community.”
  • Make sure the industry in general, or any one company in particular, is not over-represented among sponsors or conference organizers
    • Consider whether certain sponsors might be working to whitewash or silence certain areas of research. What are the interests or intentions of the organization offering you sponsorship funds? What do they hope to gain from this relationship? (2)
    • For example, the EEAMO sponsorship committee commits to “seek[ing] funding from a diverse set of sources which may include academic institutions, charitable organizations, foundations, industry, and government sources.”
  • Consider seeking alternative, industry-independent sources of funding whose interests are less likely to conflict with the subject/mission of your conference.
    • That being said, it is important to bear in mind that, as Phan et al. pointed out in their recent paper, “philanthropic foundation funding from outside Big Tech interests present different and complex considerations for researchers as producers and suppliers of ethics work.” This is why having a diversity of sources is preferable.

In working to reclaim conferences as a space of academic exploration untainted by corporate interests, the field of computer science can help to ensure that their research is better positioned to serve the best interests of the public.

(1) Several speakers backed out of their scheduled appearances at the UCLA Institute for Technology, Law & Policy’s November 2021 Power and Accountability in Tech conference after learning the center had accepted sponsorship money from the Koch Foundation, which has funded attacks on antiracist scholarship.

(2) For example, in 2016, the Computer, Privacy, & Data Protection Conference (CPDP) chose to stop accepting sponsorship funding from Palantir after participants like Aral Balkan pulled out of a panel and described CPDP’s acceptance of the company’s contributions as “privacy-washing”.

Many thanks, once again, to Prof. Arvind Narayanan for his guidance and support.