POST: Twitterature: Mining Twitter Data

Christian Howard (University of Virginia) details her work on using Twitter for literary research in a recent blog post. With her collaborator Alyssa Collins, Howard uses Python to scrape and visualize Twitter data about the usage of the term #twitterature.

#twitterature became an increasingly global term between 2011 and 2017, with a noticeable contraction in 2015 (which corresponds to the dip in usage of the hashtag during that same year). It should, of course, be noted that this hashtag is used in primarily English and French speaking countries, and no occurrences of #twitterature are recorded in South America, Russia, China, or other East Asian countries. Despite the prevalence of English in India, #twitterature likewise fails to make an appearance in this country. This dearth may be due to the lack of geospatial data that is available; alternatively, it may indicate that there are other, more popular ways of referring to literature published or disseminated via Twitter in these countries. As we continue working on this project, we’ll look into such alternative references as well as different mapping possibilities to display the data.

Howard provides a walkthrough of the scraping and visualization process, as well as a link to the project’s GitHub repository.

dh+lib Review

This post was produced through a cooperation among Jennifer Matthews, Ian Goodale, Christine Davidian, Sarah Nguyen, and Lisa Zilinski (Editors-at-large for the week), Sarah Melton (Editor for the week), and Caitlin Christian-Lamb, Nickoal Eichmann-Kalwara, Roxanne Shirazi, and Patrick Williams (dh+lib Review Editors).

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