<p>This project aimed to explore the way in which appeals for missing persons are constructed on Twitter by Greater Manchester Police, and how the public engage with these Tweets. </p>
<p>We identified features associated with public engagement with Tweets in a literature review, and then used 1,008 Tweets made by Greater Manchester Police to analyse the presence of these features, as well as the number of retweets of the Tweet messages. </p>
<p>On this page you can find the anonymised dataset of the 1,008 tweets. Anonymisation was carried out using NETANOS (Kleinberg & Mozes, 2017, Web-based text anonymization with Node.js: Introducing NETANOS (Named entity-based Text Anonymization for Open Science), Journal of Open Source Software, 2(14), 293, doi:10.21105/joss.00293).</p>
<p>All code for data preparation, analysis, and visualisation, as well as the Rmarkdown document for creating the paper are available on GitHub: <a href="https://github.com/maczokni/misperTweetsCode" rel="nofollow">https://github.com/maczokni/misperTweetsCode</a></p>
<p>This project was funded by the Manchester Statistical Society Campion Grant (<a href="https://manstatsoc.org/campion-grants/" rel="nofollow">https://manstatsoc.org/campion-grants/</a>)</p>
<p>The files here are:
- The data: 'clean_anon_misper_tweets.csv'
- The data dictionary: 'DataDictionary.txt'
- The R Markdown document 'final_paper.Rmd'</p>