What is the proportion of robots in Tweets? – BOTS
research areas
timeframe
2020 - 2021
contact
oliver.gruebner@uzh.chBackground
It is unclear how many bots influence the public conversation in social media in general and discourse on public health topics in particular.
Goals
- Identify bots in georeferenced social media data from Twitter based on Botometer, the current gold standard in bot detection.
- Apply a combined geographical and emotional trajectory analysis to evaluate potential improvements in bot detection of these data.
Methods
- Indiana University`s Botometer: Identify bots (current gold standard)
https://botometer.iuni.iu.edu/#!/ - GIS: Activity spaces and mobility indicators
- EMOTIVE, Stresscapes, LIWC: Basic emotions, overall stress, thinking styles, social concerns, and parts of speech
Publication
Edry T., N. Maani, M. Sykora, S. Elayan, Y. Hswen, M. Wolf, F. Rinaldi, S. Galea, O. Gruebner (2021). Real-time geospatial surveillance of localized emotional stress responses to COVID-19: A proof of concept analysis. Health & Place, Volume 70, https://doi.org/10.1016/j.healthplace.2021.102598