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

arXiv: https://arxiv.org/abs/2308.04887 · HTML: https://mlsanity.com/paper/2308.04887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 155 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 20 of 20)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Population Based Training of Neural Networks](https://mlsanity.com/paper/1711.09846/) (2017)
- [Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation](https://mlsanity.com/paper/1806.01156/) (2018)
- [Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence](https://mlsanity.com/paper/2001.02479/) (2020)
- [Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites](https://mlsanity.com/paper/1907.07032/) (2019)
- [Fingerprinting the Fingerprinters: Learning to Detect Browser Fingerprinting Behaviors](https://mlsanity.com/paper/2008.04480/) (2020)
- [DRAWNAPART: A Device Identification Technique based on Remote GPU Fingerprinting](https://mlsanity.com/paper/2201.09956/) (2022)
- [WebGraph: Capturing Advertising and Tracking Information Flows for Robust Blocking](https://mlsanity.com/paper/2107.11309/) (2021)
- [The Distressing Ads That Persist: Uncovering The Harms of Targeted Weight-Loss Ads Among Users with Histories of Disordered Eating](https://mlsanity.com/paper/2204.03200/) (2022)
- [The Future of Ad Blocking: An Analytical Framework and New Techniques](https://mlsanity.com/paper/1705.08568/) (2017)
- [SoK: Cryptojacking Malware](https://mlsanity.com/paper/2103.03851/) (2021)
- [Missed by Filter Lists: Detecting Unknown Third-Party Trackers with Invisible Pixels](https://mlsanity.com/paper/1812.01514/) (2018)
- [`I make up a silly name': Understanding Children's Perception of Privacy Risks Online](https://mlsanity.com/paper/1901.10245/) (2019)
- [Exploring the Cookieverse: A Multi-Perspective Analysis of Web Cookies](https://mlsanity.com/paper/2302.05353/) (2023)
- [Beyond the Front Page: Measuring Third Party Dynamics in the Field](https://mlsanity.com/paper/2001.10248/) (2020)
- [Web page classification with Google Image Search results](https://mlsanity.com/paper/2006.00226/) (2020)
- [ATOM: A Generalizable Technique for Inferring Tracker-Advertiser Data Sharing in the Online Behavioral Advertising Ecosystem](https://mlsanity.com/paper/2207.10791/) (2022)

## Built on by (most cited first, 1 of 1)

- [Detecting AI Impostors: How Do Middle Schoolers Identify LLM Agents in a Live Collaborative Setting?](https://mlsanity.com/paper/2608.30948/) (2026)

## Abstract (arXiv)

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.