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Twitter Analysis Paper

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Analyzing #MakingaMurderer with RStudio and ATLAS.ti

Amy Kochert, Alex Showalter, and Sara Spiers

Problem

In general, twitter is a platform that allows individuals and organizations to tweet their reactions or opinions to a specific event. Twitter is also used as a medium where current events can “trend”. To trend on twitter means, a “hashtag, a keyword or phrase that is precede with a pound sign (#), has become immediately popular” (Doctor, 2012). Some of these trending topics trend long enough that they are picked up by popular news outlets (i.e., CNN, Fox, MSNBC) and brought to the attention of non-twitter users. This is one-way controversial conversation propels itself from a narrow audience to a broader audience.

Making a Murderer, …show more content…
This was done by going to twitter.com and follwoing the steps to sign up for a twitter account. Next, we had to gain authorization privileges on twitter. This means we had to ask twitter to give us permission to scrap the twitter feeds. To gain permission, the software R and RStudio is needed. This was downloaded from (https://www.rstudio.com/products/rstudio/download/). There are many different versions of R and RStudio. The version needed for twitter scrapping depends on the type of computer used for the project. Once this is installed, following the steps written out by the Chodhary and Adam (2014) article. Their directions outline how to go from twitter user to twitter authorizer. Once authorization is established a few packages found in RStudio are needed. The packages to download …show more content…
At the end of the scrapping, a tota Once this code was typed into Rstudio and received the results, the results were downloaded from RStudio into a .csv file. This .csv file was our second piece of data. This .csv file was then combed through by hand removing non-substantive tweets. A decision tree was used to determine if a tweet was substantive or should be removed.

The twitter scape was designed to pull all tweets within the date range which resulted in over 8000 tweets. After scanning through the tweets in RStudio, there were several retweets. After noticing the duplicated tweets, we altered the code to exclude RTs. This narrowed down our scrapped tweet count to 2,800.

Once the first code was ran on RStudio, the result was over 8,000 tweets. After briefly reading these tweets, there was a lot of repetition of tweets and a marking of “RT” or retweet following each tweet. The code was ran again with a restriction to filter out RT in the scrapping process. This resulted in a list of a little over 2,800 tweets. These tweets were narrowed down based on a Twitter decision tree. Tweets with substantive content, a tweet with words alluding to the criminal justice system, were kept and those without substantive content were deleted. Some non-substantive content tweets would be a tweet with only hashtags, or someone simply stating they were watching the

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