Targeted and Troublesome: Tracking and Advertising on Children's Websites

• URL: http://arxiv.org/abs/2308.04887v2
• Date: Sun, 10 Dec 2023 10:14:30 GMT
• Title: Targeted and Troublesome: Tracking and Advertising on Children's Websites
• Authors: Zahra Moti, Asuman Senol, Hamid Bostani, Frederik Zuiderveen Borgesius, Veelasha Moonsamy, Arunesh Mathur, Gunes Acar
• Abstract summary: We measure the prevalence of trackers, fingerprinting scripts, and advertisements on child-directed websites. Our results show that around 90% of child-directed websites embed one or more trackers, and about 27% contain targeted advertisements. Next, we identify improper ads on child-directed websites by developing an ML pipeline that processes both images and text extracted from ads.
• Score: 10.066090482189015
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: On the modern web, trackers and advertisers frequently construct and monetize users' detailed behavioral profiles without consent. Despite various studies on web tracking mechanisms and advertisements, there has been no rigorous study focusing on websites targeted at children. To address this gap, we present a measurement of tracking and (targeted) advertising on websites directed at children. Motivated by lacking a comprehensive list of child-directed (i.e., targeted at children) websites, we first build a multilingual classifier based on web page titles and descriptions. Applying this classifier to over two million pages, we compile a list of two thousand child-directed websites. Crawling these sites from five vantage points, we measure the prevalence of trackers, fingerprinting scripts, and advertisements. Our crawler detects ads displayed on child-directed websites and determines if ad targeting is enabled by scraping ad disclosure pages whenever available. Our results show that around 90% of child-directed websites embed one or more trackers, and about 27% contain targeted advertisements--a practice that should require verifiable parental consent. Next, we identify improper ads on child-directed websites by developing an ML pipeline that processes both images and text extracted from ads. The pipeline allows us to run semantic similarity queries for arbitrary search terms, revealing ads that promote services related to dating, weight loss, and mental health; as well as ads for sex toys and flirting chat services. Some of these ads feature repulsive and sexually explicit imagery. In summary, our findings indicate a trend of non-compliance with privacy regulations and troubling ad safety practices among many advertisers and child-directed websites. To protect children and create a safer online environment, regulators and stakeholders must adopt and enforce more stringent measures.

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