BetXplain: An Explanation-Annotated Dataset for Detecting Manipulative Betting Advertisements on Social Media
MSVPJ Sathvik, Parmitha Vangapadu, Nishit Rane, Sathwik Narkedimilli, Mark Lee, Akrati Saxena
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Key claim
New dataset enables detection of manipulative betting ads.
In plain English
Imagine you're trying to help people navigate the world of online betting, especially with all the ads popping up on social media. These ads often use sneaky tactics to lure users in, which can lead to risky behavior and even affect mental health. Right now, there’s not much research on how to automatically spot these manipulative ads because there aren’t many datasets available to train models on. This is a problem because without good data, it’s hard to build systems that can effectively warn users about these risks. This is what's called a lack of annotated datasets in the field of deceptive advertising detection.
To tackle this issue, the authors created a new dataset specifically focused on betting-related advertisements from platforms like Instagram and Reddit. They didn’t just collect the ads; they also manually annotated them to highlight manipulative and deceptive practices. This means they provided labels and explanations for each ad, which is crucial for training models that can understand the nuances of persuasive tactics. By doing this, they’re laying the groundwork for future research into explainable AI methods that can detect these kinds of ads.
What’s exciting about this work is that it opens up new avenues for practical applications. For instance, they suggest that this dataset could be used to develop browser plugins that alert users when they encounter potentially manipulative betting ads. This is a step forward compared to previous work, which lacked the necessary data to build effective detection systems. Overall, this research not only contributes a valuable resource but also highlights the importance of understanding how advertising can impact mental health.
The introduction of a new dataset for detecting manipulative betting advertisements is a significant contribution to a relatively unexplored area.
The paper provides a well-annotated dataset and discusses practical applications, supporting its claims with concrete examples.
Deep reliability assessment
The work solidly supports that an explanation-annotated dataset (Instagram/Reddit, primarily English) enables descriptive analysis of persuasion tactics and moderate supervised detection performance. It likely overclaims causal links to mental health risk and practical deployment readiness (e.g., regulatory crawlers, browser warnings) without out-of-domain, multimodal, or longitudinal user-level validation.
Reproducibility
Dataset: not yet public (promised upon acceptance; link omitted for anonymization). Code: not mentioned. Annotations and procedures are described, but no URLs or artifacts are provided, limiting immediate reproduction.
Key figure
A multi-panel visualization showing keyword heatmaps, psychological trigger composition, and sentiment density overlaps that illustrate reward/urgency dominance and a ‘deceptive positivity’ effect across betting ad categories.