Main content



Loading wiki pages...

Wiki Version:
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. 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. 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). All code for data preparation, analysis, and visualisation, as well as the Rmarkdown document for creating the paper are available on GitHub: This project was funded by the Manchester Statistical Society Campion Grant ( The files here are: - The data: 'clean_anon_misper_tweets.csv' - The data dictionary: 'DataDictionary.txt' - The R Markdown document 'final_paper.Rmd'
OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.