Media (in)visibility of violence in Manguinhos, Rio de Janeiro, Brazil (2020–2024): a statistical modeling approach

Introduction This study investigates how the visibility of different types of violent events in Manguinhos (Rio de Janeiro) evolved in online media between 2020 and 2024, and compares these patterns with community-reported data from Fogo Cruzado. Methods Two datasets were analyzed: online news articles collected via the Meltwater ® platform (through Arquimedes Social ® ) and classified into four categories, police confrontation, homicide/death, drug trafficking, and generic violence, and verified incident records from Fogo Cruzado. Analyses included log–log transformations with power-law assessment to explore long-term patterns, Negative Binomial Regression (NB) to test temporal trends, and Interquartile Range (IQR) smoothing to detect anomalous peaks in coverage. Only the “police confrontation” category showed a statistically significant upward trend in media visibility, with an average growth of approximately 3.6% per month, consistent with power-law dynamics. The other categories exhibited no significant trends, indicating more stable or sporadic reporting. IQR analysis identified five peaks in coverage, likely linked to large-scale police operations or high-profile incidents. Results Findings suggest that police confrontation coverage follows a self-reinforcing cycle shaped by state interventions and exceptional events, while other violence types remain comparatively underreported. This visibility asymmetry underscores how journalistic narratives selectively amplify certain forms of violence, potentially shaping public perceptions, legitimizing specific security approaches, and obscuring structural drivers. Conclusion By integrating quantitative modeling with anomaly detection, this study provides a replicable framework for monitoring media cycles, assessing visibility disparities, and informing debates on representation, accountability, and policy responses in contexts of chronic urban violence.