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Analysis examines crime news shared on social media in the three months prior to the 2016 presidential election.
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codebook
data
topicmodeling
Readme.md
cleantweets.R
collectnewswebsites.R
exclusiondictionary.R
expandurls.R
finalizedata.R
twitterscrape.R

Readme.md

Biased racial (mis)representations and news frames on social media

This study content analyzes crime news disseminated on Twitter in the three months leading to the 2016 presidential election (2016-08-08 to 2016-11-08). This project presents computational methods capable of analyzing groups casts as criminals in crime news frames and biased language within these news frames. Analysis incorporates topic modeling, supervised machine learning, and natural language processing. This git repository contains R code necessary for replicating analysis.

Twitter IDs for the sample are avaiable in this git repository.

R Code

  1. Topic Modeling: https://github.com/marisaasmith/Biased-racial-mis-representations-project/tree/master/topicmodeling
  2. Exclusion Dictionary: https://github.com/marisaasmith/Biased-racial-mis-representations-project/blob/master/exclusiondictionary.R
  3. Remove Irrelavent Tweets: https://github.com/marisaasmith/Biased-racial-mis-representations-project/blob/master/cleantweets.R
  4. Scrape Twitter: https://github.com/marisaasmith/Biased-racial-mis-representations-project/blob/master/twitterscrape.R
  5. Wrangle Data: https://github.com/marisaasmith/Biased-racial-mis-representations-project/blob/master/finalizedata.R
  6. Expand URLs: https://github.com/marisaasmith/Biased-racial-mis-representations-project/blob/master/expandurls.R
  7. Detect News Websites: https://github.com/marisaasmith/Biased-racial-mis-representations-project/blob/master/collectnewswebsites.R
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