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In this study, we conduct the first Big Data content analysis of local News in the US, relying on more than 500,000 word-for word transcripts stemming from a new, proprietary data base and state-of-the-art Bayesian text modeling. We document that the local news agenda is mainly driven by traffic, weather, sports and other local news, and crime. In a second step, we assess to what extent consumer demand drives the local news agenda, and find substantive associations for traffic reporting and national news reporting.