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.
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