NLP: Natural Language Propaganda


Who are the targets of insurgent propaganda? I investigate the ability to classify the targets (e.g, the U.S. or Kabul) of insurgent propaganda messages using a novel corpus containing over 11,000 Taliban statements from 2014 to 2020. In experiments with Convolutional Neural Network (CNN) and transformer architectures, I demonstrate that the audiences of insurgent messages are best captured by transformers, likely owing to its encoder-decoder architecture. This paper’s contribution is twofold: First, it offers a new and novel data set with utility in classification and summarization tasks for machine learning. Second, it suggests that since the audience of messaging can be reliably identified, new opportunities are afforded to analysts to look closer at the contrasts in language to better understand the targets of information.



How healthy is the average Instacart user? Are certain types (i.e., vegetarians, carnivores) of food buyers healthier than others? I bring new data to bear on these questions to better understand how healthy the average Instacart user is and to better understand the health benefits afforded to Instacart users who choose some types (i.e., plant-based, meat-based) of foods over others. To determine the relative health of Instacart users, I matched the top 10 most ordered products by aisle with USDA nutrient data by using USDA-provided API access to their database through JavaScript Object Notation (JSON).

Map Off


Map Off is a game designed to test your geography skills in the United States or around the World. The inspiration for this game comes from my wife, Hannah, because we often test our spatial skills against one another in the presence of a map. In turn, we now have access to maps and competition anytime we want.