The Hidden Cost of Digital Advertising: A Proactive Approach to Brand-Safety Through Automated Content Screening

Digital advertising faces a persistent challenge: brand-safety incidents and ad disapprovals cost companies billions annually while damaging consumer trust. Current approaches predominantly rely on reactive measures, focusing on where ads are placed rather than proactively screening what advertisers publish. This research addresses this gap by proposing and validating a lightweight, pre-publication screening system that combines toxicity detection and sentiment analysis to identify high-risk creative content before publication.Through empirical analysis of 5,000 Wikipedia talk page comments as a proxy for diverse online content, this study demonstrates that a dual-threshold screening system (toxicity >0.7 and sentiment <-0.5) can effectively categorize content into three distinct risk levels. The findings reveal a clear tri-modal distribution: 66.0% low-risk content suitable for auto-approval, 22.9% medium-risk content requiring human review, and 11.1% high-risk content warranting automatic rejection. High-risk content exhibited extreme values on both dimensions (average toxicity: 0.982, average sentiment: -0.856) and contained explicit policy violations, including personal attacks, hate speech, and threats.The proposed system achieved 92.9% classification accuracy with a 7.1% false positive rate, outperforming industry benchmarks by 8-15 percentage points. Implementation would reduce manual review workload by 77.1% while ensuring 100% of high-risk content is prevented from publication. Performance validation through 10-fold cross-confirmation showed remarkable stability (SD = ±0.2%), indicating robust generalization across content types.These findings have significant implications for digital advertising practice. By shifting brand-safety from reactive damage control to proactive risk prevention, organizations can substantially reduce ad disapprovals, protect brand equity, and optimize resource allocation. The research provides empirically-validated thresholds and a scalable technical architecture for immediate implementation, offering a cost-effective solution to one of digital marketing's most persistent challenges.