BetXplain：用于检测社交媒体上操纵性赌博广告的解释注释数据集

Computer Science > Machine Learning

arXiv:2606.27274v1 (cs) [Submitted on 25 Jun 2026 (this version), latest version 27 Jun 2026 (v2)]

Title:BetXplain: An Explanation-Annotated Dataset for Detecting Manipulative Betting Advertisements on Social Media

Authors:MSVPJ Sathvik, Parmitha Vangapadu, Nishit Rane, Sathwik Narkedimilli, Mark Lee, Akrati Saxena View a PDF of the paper titled BetXplain: An Explanation-Annotated Dataset for Detecting Manipulative Betting Advertisements on Social Media, by MSVPJ Sathvik and 5 other authors View PDF HTML (experimental)

Abstract:The promotion of betting applications on social media platforms has increased significantly in recent years. Many of these advertisements use persuasive techniques that may mislead users, encourage risky behavior, and potentially influence users' mental well-being. However, research on the automated detection of manipulative and deceptive betting advertisements remains limited due to the lack of publicly available annotated datasets. In this work, we introduce a new dataset of betting-related advertisements collected from two widely used social media platforms, Instagram and Reddit. The advertisements were manually annotated for manipulative and deceptive advertising practices. In addition to classification labels, the dataset includes human-provided explanations that describe the reasoning behind each annotation, enabling research into explainable approaches to detecting manipulative advertising. Furthermore, we analyze the strategies commonly used in betting advertisements and examine how these persuasive tactics may impact users' mental health. The proposed framework can also enable practical applications such as browser plugins that warn users about manipulative betting advertisements and automated web crawlers that help regulatory authorities monitor and detect such promotions online.

Subjects:	Machine Learning (cs.LG)
Cite as:	arXiv:2606.27274 [cs.LG]
	(or arXiv:2606.27274v1 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2606.27274 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Mandava Sathvik [view email]
[v1] Thu, 25 Jun 2026 16:51:53 UTC (9,825 KB)
[v2] Sat, 27 Jun 2026 09:56:16 UTC (9,825 KB)
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