Abstract
This paper analyzes a new social media phenomenon in which users are lying about not being bots or about real news being fake news. Twitter data were collected throughout the 2019 Canadian federal election cycle, and we investigated the use of the #FakeNews and #NotABot hashtags. Twitter users connected the #FakeNews hashtag more often to mainstream news sources and reporters rather than actual fake news sites, often as a way to discredit certain reporters or viewpoints. We also found that users of the #NotABot hashtag were no more likely to be human than other users participating in political discourse in our data set. Bots that attempt to pass as human have been reportedly used to amplify misinformation campaigns in the past. This new type of online defensive strategy shows how these campaigns continue to evolve and illustrates how they may be run in the future.
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Acknowledgements
This work was supported in part by the Office of Naval Research (ONR) Award 00014182106 and the Center for Computational Analysis of Social and Organization Systems (CASOS). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ONR or the U.S. government. The authors would also like to thank David Beskow for collecting the data used in this study and for running his BotHunter algorithm on the data. We would also like to thank Binxuan Huang for access to his Twitter user classification system.
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King, C., Bellutta, D., Carley, K.M. (2020). Lying About Lying on Social Media: A Case Study of the 2019 Canadian Elections. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A., Hussain, M. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2020. Lecture Notes in Computer Science(), vol 12268. Springer, Cham. https://doi.org/10.1007/978-3-030-61255-9_8
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DOI: https://doi.org/10.1007/978-3-030-61255-9_8
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