Arxiv.org/pdf/1603.0 3099.pdf

1603.03099.pdf
Preview of arxiv.org/pdf/1603.0 3099.pdf
🔗 Source: arxiv.org
📊 Size: 466 KB
📄 Pages: 4 pages
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Summary

Researchers analyzed Donald Trump's Twitter activity from September 18th to December 27th, 2015, to infer the topic preferences of his followers. They used latent Dirichlet allocation (LDA) to extract topics from Trump's tweets and negative binomial regression to model the "likes" each tweet received. The study found that attacking Democrats, such as President Obama and Hillary Clinton, earned Trump the most "likes." The framework used in this study can be applied to analyze the topic preferences of other politicians' followers on Twitter.

Description

Researchers analyzed Donald Trump's Twitter activity from September 18th to December 27th, 2015, to infer the topic preferences of his followers.

Technical Information

  • File Format: PDF
  • File Size: 466 KB
  • Pages: 4
  • Language: EN
  • Total Downloads: 237
  • Last Updated: 1 month ago

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