In this challenge you will practice exploring a new text dataset. The folder "../data/facebook-senate" contains the posts published on the Facebook pages of Senators in the U.S. Congress between 2013 and 2014.

### PART 1:

Read the dataset into R using readtext. Who posted the most text in their Facebook page?

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What are the most common features in this corpus?

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Who is the senator with the highest readability score?

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Pick a senator (or group of senators) and find the features with the highest keyness.

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Now, extract the metadata about the party name from the file names and compute keyness again for Republicans vs Democrats.

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### PART 2:

Run LDA with the entire corpus. Look at the words associated to a few topics. What are the general themes in this dataset?

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Extract gender from the file name and run STM for a few iterations with this feature as a covariate for prevalence.

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Look at how gender predicts prevalence for a few topics. What do you find? Are there any large differences?

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