Measuring the Interference Effect of Bots in Disseminating Opposing Viewpoints Related to COVID-19 on Twitter Using Epidemiological Modeling

Simple item page
dc.contributor.author Maleki, Maryam
dc.contributor.author Mead, Esther
dc.contributor.author Arani, Mohammad
dc.contributor.author Agarwal, Nitin
dc.date.accessioned 2022-12-27T19:03:20Z
dc.date.available 2022-12-27T19:03:20Z
dc.date.issued 2023-01-03
dc.description.abstract The activity of bots can influence the opinions and behavior of people, especially within the political landscape where hot-button issues are debated. To evaluate the bot presence among the propagation trends of opposing politically-charged viewpoints on Twitter, we collected a comprehensive set of hashtags related to COVID-19. We then applied both the SIR (Susceptible, Infected, Recovered) and the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological models to three different dataset states including, total tweets in a dataset, tweets by bots, and tweets by humans. Our results show the ability of both models to model the diffusion of opposing viewpoints on Twitter, with the SEIZ model outperforming the SIR. Additionally, although our results show that both models can model the diffusion of information spread by bots with some difficulty, the SEIZ model outperforms. Our analysis also reveals that the magnitude of the bot-induced diffusion of this type of information varies by subject.
dc.format.extent 10
dc.identifier.doi 10.24251/HICSS.2023.284
dc.identifier.isbn 978-0-9981331-6-4
dc.identifier.uri https://hdl.handle.net/10125/102915
dc.language.iso eng
dc.relation.ispartof Proceedings of the 56th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Digital Methods
dc.subject botometer
dc.subject covid-19
dc.subject epidemiological modeling
dc.subject misinformation
dc.subject social network analysis
dc.title Measuring the Interference Effect of Bots in Disseminating Opposing Viewpoints Related to COVID-19 on Twitter Using Epidemiological Modeling
dc.type.dcmi text
prism.startingpage 2296
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
0224.pdf
Size:
771.64 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Digital Methods