3 research outputs found
Recommended from our members
Messengers: A Content Analyses of the Billboard Rap Charts Number One Songs from 2000 to 2017
Rap music, which originated under hip hop, has become one of the most popular genres of music in the United States. From the beginnings of hip hop, the hip hop generation was named, and a movement started. There has been much research on both hip hop and rap music and the messages that this radical genre of music produced. While content analyses have been done in the past, it is necessary to continue to analyze this music, as it is incredibly prevalent and impactful in the United States. These analyses are important in terms of examining commercial success, mainstream accessibility, and the messages and themes that are present in one of the most popular genres of music. This thesis examines the content of the rap songs that reached number one on the Billboard rap chart, from the years 2000 to 2017, providing both a historical and contemporary view of this music that has reached the pinnacle of success among the genre of rap music since the turn of the century. The findings document demographics of the artists and themes that are present in the most popular rap songs; indicating consistency in messages across the sample, with popular rap songs having many references to deviant behavior, explicit word use, and negative references to women, while lacking in socially conscious themes and pro-women themes. However, rap songs have also provided some powerfully consciousness-raising about important social issues. Overall, the themes that “plague” rap music are present in the most popular rap songs, and this has been consistent with time in the United States
The United States COVID-19 Forecast Hub dataset
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages