Here’s why sketchy ads appear on legit websites
A computer scientist explains where those ads for clickbait articles, miracle weight loss supplements, and get-rich quick schemes come from.
Programmatic advertising, explained
The modern online advertising marketplace is meant to solve one problem: match the high volume of advertisements with the large number of ad spaces. The websites want to keep their ad spaces full and at the best prices, and the advertisers want to target their ads to relevant sites and users.
Rather than each website and advertiser pairing up to run ads together, advertisers work with demand-side platforms, tech companies that let advertisers buy ads. Websites work with supply-side platforms, tech companies that pay sites to put ads on their page. These companies handle the details of figuring out which websites and users should be matched with specific ads.
Most of the time, ad tech companies decide which ads to show through a real-time bidding auction. Whenever a person loads a website, and the website has a space for an ad, the website’s supply-side platform will request bids for ads from demand-side platforms through an auction system called an ad exchange. The demand-side platform will decide which ad in their inventory best targets the particular user, based on any information they’ve collected about the user’s interests and web history fromtracking users’ browsing, and then submit a bid. The winner of this auction gets to place their ad in front of the user. This all happens in an instant.
Big players in this marketplace include Google, which runs a supply-side platform, demand-side platform and an exchange. These three components make up an ad network. A variety of smaller companies such as Criteo, Pubmatic, Rubicon and AppNexus also operate in the online advertising market.
This system allows an advertiser to run ads to potentially millions of users, across millions of websites, without needing to know the details of how that happens. And it allows websites to solicit ads from countless potential advertisers without needing to contact or reach an agreement with any of them.
Screening out bad ads: an imperfect system
Malicious advertisers, like any other advertiser, can take advantage of the scale and reach of programmatic advertising to send scams and links to malware to potentially millions of users on any website.
There are some checks against bad ads at multiple levels. Ad networks, supply-side platforms and demand-side platforms typically have content policies restricting harmful ads. For example, Google Ads has an extensive content policy that forbids illegal and dangerous products, inappropriate and offensive content, and a long list ofdeceptive techniques, such as phishing, clickbait, false advertising and doctored imagery.
However, other ad networks have less stringent policies. For example, MGID, anative advertisingnetwork my colleagues and I examined fora studyand found to run many lower-quality ads, has a much shortercontent policythat prohibits illegal, offensive and malicious ads, and a single line about “misleading, inaccurate or deceitful information.” Native advertising is designed to imitate the look and feel of the website that it appears on, and is typically responsible for the sketchy looking ads at the bottom of news articles. Another native ad network, content.ad, has no content policy on their website at all.
Websites can block specific advertisers and categories of ads. For example, a site could block a particular advertiser that has been running scammy ads on their page, or specific ad networks that have been serving low-quality ads.
However, these policies are only as good as the enforcement. Ad networks typically use a combination of manual content moderators and automated tools to check that each ad campaign complies with their policies. How effective these are is unclear, buta report by ad quality firm Confiantsuggests that between 0.14% and 1.29% of ads served by various supply-side platforms in the third quarter of 2020 werelow quality.
Malicious advertisers adapt to countermeasures and figure out ways to evade automated or manual auditing of their ads, or exploit gray areas in content policies. For example, ina studymy colleagues and I conducted on deceptive political ads during the 2020 U.S. elections, we found many examples of fake political polls, which purported to be public opinion polls but asked for an email address to vote. Voting in the poll signed the user up for political email lists. Despite this deception, ads like these may not have violated Google’s content policies for political content, data collection or misrepresentation, or were simply missed in the review process.
Bad ads by design: native advertising on news websites
Lastly, some examples of “bad” ads are intentionally designed to be misleading and deceptive, by both the website and ad network. Native ads are a prime example. They apparently are effective because native advertising companiesclaimhigher clickthrough rates and revenue for sites.Studieshaveshownthat this is likely because users have difficulty telling the difference between native ads and the website’s content.
You may have seen native ads on many news and media websites, including on major sites like CNN, USA Today and Vox. If you scroll to the bottom of a news article, there may be a section called “sponsored content” or “around the web,” containing what look like news articles. However, all of these are paid content. My colleagues and I conducted astudyon native advertising on news and misinformation websites and found that these native ads disproportionately contained potentially deceptive and misleading content, such as ads for unregulated health supplements, deceptively written advertorials, investment pitches and content fromcontent farms.
This highlights an unfortunate situation. Even reputable news and media websites are struggling to earn revenue, and turn to running deceptive and misleading ads on their sites to earn more income, despite the risks it poses to their users and the cost to their reputations.
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This article byEric Zeng, PhD Candidate in Computer Science & Engineering,University of Washington, is republished fromThe Conversationunder a Creative Commons license. Read theoriginal article.
Story byThe Conversation
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