Readers of Right-Wing Critics of American Conservatism may have noted that the so-called Alt-Right (short for alternative right) was not discussed explicitly in the text. This is largely because that curious movement was apparently in a dormant period as I wrote the book (mostly in 2014) -- the original creators of the term had largely stopped using it at that time, and the website bearing that name had been recently shut down. I thought the term was pretty much done for. I submitted my final draft of the book in early 2015, before the Alt-Right saw a massive revival on social media. In any event, I will have much more to say about this if/when I get a contract to publish the follow-up book.
Some readers may not know what the Alt-Right is (and if you aren't on social media, that's quite likely). I don't really care to explain it here, but I don't think it's unfair to say that it is predominantly a white nationalist movement. If you are unfamiliar with the term and want to know more, plenty of journalists have discussed this movement (see here, here, and here, as well as plenty of other places if you just enter the term into a search engine).
Anyway, since this a predominantly online phenomenon, I thought I could make a little sense of the Alt-Right by downloading a huge number of tweets using the #AltRight hashtag. I did this using the TwitteR package in R (which is actually a very cool package). I then deleted all the most common words ("the", "and", "he", etc.) and looked at what words popped up the most. A more visually appealing way to do this is to create a word cloud (which can also be done in R ... actually, you can do pretty much anything in R).
The following word cloud is based on about 5,000 tweets that were posted in mid-April, the height of primary season. Larger words occurred with greater frequency. I am posting it here without commentary, but I suspect that neither supporters of the Alt-Right nor that movement's most vocal opponents will be surprised by the results.
I actually have a much larger number of Tweets using that hashtag saved, and have made a word cloud using those. It looks pretty much the same, except in more recent months "white" overtook "Trump" as the most common word.