A recent study published by researchers from the School of Information Studies (iSchool) reveals that the general public has a poor understanding of the workings of online behavioral advertising, and the privacy implications behind the information that advertisers gather.
The research behind the paper, Folk Models of Online Behavioral Advertising, conducted by Assistant Professor Yang Wang and Ph.D. student Yaxing Yao, found that two-thirds of the consumers they interviewed in the study did not realize that most online advertising involved third-party entities and advertising networks that track a user's browsing activities across websites to provide targeted ads.
Online behavioral advertising refers to the practices that companies and retailers engage in to collect information, usually with the help of third-party ad networks, about a consumer's online activities and the use of this information to display relevant ads to the consumer.
This poor understanding of third-party data collection, the types of data collected and the way ad blocking software and tools work at the browser level prevents consumers from having any meaningful say in what data is collected by these advertising networks. This in turn often results in the adoption of ad blocking software and browser plugins, which are detrimental to advertisers.
"One of the interesting things that we found in this research," says Wang, "is that the majority of the people we interviewed were more concerned about what types of personal information was being collected on them, rather than who was collecting it."
This discovery has implications for how consumers block advertisers and advertising networks from their browser, Wang explains.
"Right now, ad blocking software just provides a list of third-party trackers on a website, and then users can block or allow what trackers can be used on that particular site, and generally consumers just block all the trackers," he says.
"But," continues Wang, "if these tools operated on an information-based blocking model, rather than a tracker-based model, consumers could have more control over the information they share, and advertisers could still receive some data that consumers are willing to share and this data would help them target the right ads to consumers. It's a compromise with benefits for both sides."
Wang recently spoke about this topic at the Federal Trade Commission's (FTC) PrivacyCon event in Washington, D.C. The event encourages collaboration among leading researchers, academics, industry representatives, consumer advocates and the government to address the privacy and security implications of emerging technologies.
At the conference, Wang advocated for moving the online advertising industry into an information-based blocking model.
"In order to realize this new model, trackers will need to clearly state what kinds of information they are collecting on consumers, and this is largely absent now," says Wang. "I made the suggestion at the FTC event that the industry should have best practices that address this, or the FTC should step in and require this information be provided by the tracking companies."
"In order to see this be a realistic implementation in the future of online advertising, you'll need some sort of sea change in how advertisers address their privacy policies," Wang notes, "and my guess is that the advertising industry would push back at first, because consumers would be alarmed to know exactly what data is being collected."
However, the current model of ad blockers in use now is inherently bad for the advertisement industry.
"It destroys their revenue model, they can't serve ads when their trackers are being blocked," said Wang. "So it is in their best interests to understand this problem and address it. Our research shows that consumers would be more comfortable with an information-based blocker where they control what they share, and the advertising industry still has access to some level of data that allows them to provide targeted ads."
Explore further: Finding an online advertising compromise
Folk Models of Online Behavioral Advertising: www.ftc.gov/system/files/docum … /10/00030-129045.pdf