Mapping violence perceptions through YouTube comments: a new approach to real-time violence monitoring

This paper introduces the Violence Perception Index (VPI), a novel methodology for quantifying violence-related discourse through geolocated YouTube comments. Utilizing the YouTube API and natural language processing techniques, the VPI measures public references to violence across 1.2 million unique geolocated videos in Mexico (2020–2024), extracting 14.8 million comments from over 500,000 videos with user engagement. This approach provides spatiotemporally granular data on violence-related discourse, which we treat as a proxy for violence perceptions, extending beyond traditional event-based datasets by capturing not only documented violence but also rumors, fears, and community discourse about violence, dimensions that influence community behavior and social stability independently of official records. Violence scores are constructed using a weighted Spanish-language dictionary developed through semantic network expansion from violence-related seed terms. The dictionary-based scoring approach demonstrates moderate-to-substantial agreement with large language model classifications across 700 stratified comments (75-81% agreement), validating the method’s capacity to systematically identify violence-related discourse at scale while maintaining computational efficiency for processing millions of comments. The VPI is benchmarked against established violence indicators including ACLED fatalities and official municipal homicide statistics through panel regression specifications incorporating comprehensive spatial and temporal fixed effects. Analysis reveals systematic geographic heterogeneity: the VPI correlates strongly with ACLED data in high-population areas but exhibits stronger correlation with official homicide records in low-population contexts. Rather than constituting a methodological limitation, this pattern demonstrates the VPI’s enhanced sensitivity in marginalized and remote regions where news-based datasets suffer from systematic reporting bias. The methodology is immediately scalable across languages and geographies, providing complementary intelligence for conflict monitoring, early warning systems, and policy interventions in precisely those underrepresented areas where traditional event-based monitoring systems provide incomplete coverage.