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<h1>Welcome!</h1> <p>This is the OSF page Matthew Jordan's Master's thesis in Experimental Psychology at Oxford University, titled "Mean Tweets: The Role of Emotion in the Spread of Moral Ideas on Twitter." </p> <hr> <p>This project is a replication of <a href="https://osf.io/59uyz/" rel="nofollow">(Brady et al., 2017)</a>, a paper out of NYU that demonstrated that the presence of moral-emotional words in tweets makes them more likely to be retweeted. <br> <br></p> <p>In the spirit of open science and reproducilibty, I have made all my code and data available on this page. The main data set is <a href="https://osf.io/cwbhm/" rel="nofollow">filtered_all.RDS</a>, located in the <strong>Cleaned Files</strong> folder. You can view these data with <a href="https://www.rstudio.com/" rel="nofollow">RStudio</a> by entering the following: <br><br></p> <pre class="highlight"><code>setwd(&quot;/Downloads/Tweets&quot;) # Wherever you put the file filtered_all &lt;- readRDS(&quot;filtered_all.RDS&quot;)</code></pre> <p><br> You can now explore this big data set and re-run the analyses I performed. All of the scripts I used to analyze the data are located in the <strong>R Code</strong> folder. </p> <h2>Collecting Your Own Data</h2> <p>You can collect your own tweets from Twitter's API and run the same analyses to examine moral/emotional language. The steps are as follows: <br><br> 1. Follow <a href="https://github.com/mkearney/rtweet" rel="nofollow">these instructions</a> to create a Twitter App, which you'll need to use <code>rtweet</code> and gather data.</p> <ol> <li> <p>Modify <code>Matthew_Thesis_Data_Collection.R</code> with your personal Twitter App information.</p> </li> <li> <p>Specify keywords and an amount of time to collect for. </p> </li> </ol> <p><br></p> <p>And you're off! The <code>rtweet</code> package saves its output as <code>.JSON</code> files. You can now use <a href="https://osf.io/5b9wm/" rel="nofollow">Matthew_Thesis_Data_Preparation.R</a> to parse these files and turn them into <code>.csv</code> files. </p> <p><br></p> <p>The <code>.csv</code> files can in turned be read into R and analyzed using the functions in <a href="https://osf.io/nrgz9/" rel="nofollow">Matthew_Thesis_Data_Analysis.R</a>. Before you know it, you'll have pretty much completed a Master's thesis.</p> <p><br></p> <p>Note that in order to use these two functions, you will also have to download the "Helper" files, which contain useful functions.</p>
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