Hijacking online reviews: sparse manipulation and behavioral buffering in popularity-biased rating systems

Computer Science > Social and Information Networks [Submitted on 16 Mar 2026] Hijacking online reviews: sparse manipulation and behavioral buffering in popularity-biased rating systems Itsuki Fujisaki, Kunhao Yang Online reviews and recommendation systems help users navigate overwhelming choice, but they are vulnerable to self-reinforcing distortions. This paper examines how a single malicious reviewer can exploit popularity-biased rating dynamics and whether behavioral heterogeneity in user responses can reduce the damage. We develop a minimal agent-based model in which users choose what to rate partly on the basis of currently displayed averages. We compare broad attacks that perturb many items with sparse attacks that selectively boost low-quality items and suppress high-quality items. Additional analyses not shown here indicate that sparse attacks are substantially more harmful than broad attacks because they better exploit popularity-based exposure. The main text then focuses on sparse attacks and asks how their effects change as the fraction of contrarian users increases. Three results stand out. First, attack-induced damage is strongest when prior honest reviews are scarce, revealing a transition from a fragile low-information regime to a more robust high-information regime. Second, sparse attacks are especially effective at artificially promoting low-quality items. Third, moderate contrarian diversity partially buffers these distortions, primarily by suppressing the rise of low-quality items rather than fully restoring high-quality items to the top. The findings suggest that recommendation robustness depends not only on attack detection and predictive accuracy, but also on review density, popularity feedback, and user response heterogeneity. Comments: 18page, 3figures Subjects: Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.13049 [cs.SI]   (or arXiv:2604.13049v1 [cs.SI] for this version)   https://doi.org/10.48550/arXiv.2604.13049 Focus to learn more Submission history From: Itsuki Fujisaki [view email] [v1] Mon, 16 Mar 2026 07:32:33 UTC (2,746 KB) Access Paper: view license Current browse context: cs.SI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)