"And We Will Fight For Our Race!" A Measurement Study of Genetic Testing Conversations on Reddit and 4chan
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Abstract (arXiv)
Progress in genomics has enabled the emergence of a booming market for "direct-to-consumer" genetic testing. Nowadays, companies like 23andMe and AncestryDNA provide affordable health, genealogy, and ancestry reports, and have already tested tens of millions of customers. At the same time, alt- and far-right groups have also taken an interest in genetic testing, using them to attack minorities and prove their genetic "purity." In this paper, we present a measurement study shedding light on how genetic testing is being discussed on Web communities in Reddit and 4chan. We collect 1.3M comments posted over 27 months on the two platforms, using a set of 280 keywords related to genetic testing. We then use NLP and computer vision tools to identify trends, themes, and topics of discussion. Our analysis shows that genetic testing attracts a lot of attention on Reddit and 4chan, with discussions often including highly toxic language expressed through hateful, racist, and misogynistic comments. In particular, on 4chan's politically incorrect board (/pol/), content from genetic testing conversations involves several alt-right personalities and openly antisemitic rhetoric, often conveyed through memes. Finally, we find that discussions build around user groups, from technology enthusiasts to communities promoting fringe political views.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jan 2019.
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Citing papers per year: 2021: 3; 2022: 4; 2023: 2; 2024: 0; 2025: 0; 2026: 1.
10 papers.
- 2023"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Modelscited by 457
- 2021Detecting Harmful Memes and Their Targetscited by 77
- 2022Detecting and Understanding Harmful Memes: A Surveycited by 58
- 2021Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Gocited by 32
- 2022Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbotscited by 31
- 2022On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learningcited by 26
- 2022DISARM: Detecting the Victims Targeted by Harmful Memescited by 20
- 2021Hostility Detection in Hindi leveraging Pre-Trained Language Modelscited by 6
- 2023minOffense: Inter-Agreement Hate Terms for Stable Rules, Concepts, Transitivities, and Latticescited by 1
- 2026Gendered Pathways in AI Companionship: Cross-Community Behavior and Toxicity Patterns on Redditcited by 1
- 2026Gendered Pathways in AI Companionship: Cross-Community Behavior and Toxicity Patterns on Reddit3 Jan 2026
- 2023"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models7 Aug 2023
- 2023minOffense: Inter-Agreement Hate Terms for Stable Rules, Concepts, Transitivities, and Lattices29 May 2023
- 2022On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning13 Dec 2022
- 2022Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots7 Sep 2022
- 2022DISARM: Detecting the Victims Targeted by Harmful Memes11 May 2022
- 2022Detecting and Understanding Harmful Memes: A Survey9 May 2022
- 2021Detecting Harmful Memes and Their Targets24 Sep 2021
- 2021Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go27 Feb 2021
- 2021Hostility Detection in Hindi leveraging Pre-Trained Language Models14 Jan 2021