Overview of data sets provided in this repository
Teaching text analysis with Twitter data in 2021 and 2022
In 2021 and 2022, the coordinators of the Machines of Knowledge course taught computational text analysis with Twitter data because the Twitter API allowed allowed user-friendly access to a large amount of social media data on current topics. Among the topics that we explore in class in winter 2022 were the attack on a gay bar in Oslo, ethical debates around the FIFA Worldcup in Qatar, and Elon Musk's contested acquisition of Twitter:
- Twitter reactions to the 2022 attack on a gay bar in Oslo (ca. 50000 tweets)
- Twitter reactions to the 2022 FIFA Worldcup in Qatar (ca. 100000 tweets)
- Twitter reactions to Elon Musk's takeover of the platform between October 28 and October 31
Working with these data sets while full academic API access was still possible revealed increasing flaws of Twitter data that made us question whether this type of data was at all suitable for teaching and student research. Among the observations that we made with our students were the high amount of spam and bot posts piggy-backing on every trending hashtag, the many cryptocurrency and NFT advertisements distorting our data, and the problematic tone and quality of many contributions.
Exploring Twitter data from a data feminist perspective
Exploring alternative data sources after 2023
In 2022, the take-over of Twitter by Elon Musk resulted in fundamental changes of the platform's academic and educational policy, which forced us to explore new data sources in the 2023/2024 academic year.
- App Store comments about successful true crime podcasts
- YouTube reactions to Judith Butler's video lecture on intersectionality (ca. 2000 comments)
- YouTube comments on Amanda Gorman's inaugural poem (ca. 8000 comments)
Case studies for teaching since 2024
Since 2024, Monika Barget has integrated data stilled used for teaching the MA Digital Cultures course Machines of Knowledge at Maastricht University into more elaborate case studies that also offer students historical and cultural background information. These case studies, which include recommendations for different data sources per topic, are regularly updated in Github:
- Body Image
- Domestic Violence
- Elon Musk
- Girlboss
- Epstein
- Menstruation
- Miley Cyrus
- Race
- Seretse Khama
- Slavery
- Soraya Esfandiary
- True Crime
- Waste Colonialism
- Witchcraft
How to ingest these data sets into Voyant Tools
In order to analyse the provided data in Voyant Tools, first go to the Voyant Tools website and simply paste the raw URL of the linked data into the "add text" field. The raw URL on Github is displayed when you open an individual data set in a Github repository and then click on the raw button in the top right corner. You can then copy the raw URL from the address bar of your browser. Once you have inserted the raw URL in the "add text" field in Voyant, you can press the blue "reveal" button and start exploring the data set.