Time-Dynamics of (Mis)Information Spread on Social Networks: A COVID-19 Case Study
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-53503-1_13
Reference22 articles.
1. Ahvanooey, M.T., Zhu, M.X., Mazurczyk, W., Choo, K.K.R., Conti, M., Zhang, J.: Misinformation detection on social media: challenges and the road ahead. IT Professional 24(1), 34–40 (2022). https://doi.org/10.1109/MITP.2021.3120876
2. De Meo, P., Ferrara, E., Fiumara, G., Provetti, A.: Generalized louvain method for community detection in large networks. In: 2011 11th International Conference on Intelligent Systems Design and Applications, pp. 88–93 (2011). https://doi.org/10.1109/ISDA.2011.6121636
3. DeVerna, M.R., Aiyappa, R., Pacheco, D., Bryden, J., Menczer, F.: Identification and characterization of misinformation superspreaders on social media. CoRR abs/2207.09524 (2022). https://doi.org/10.48550/arXiv.2207.09524
4. DeVerna, M.R., et al.: Covaxxy: a collection of english-language twitter posts about covid-19 vaccines. In:Proceedings of the International AAAI Conference on Web and Social Media 15(1), 992–999 (2021). https://ojs.aaai.org/index.php/ICWSM/article/view/18122
5. Duzen, Z.: covaxxy-data-mining. https://github.com/duzenz/covaxxy-data-mining (2023). Accessed June 9 2023
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