- Fast growth in Twitter activity starting mid-January, following log curves
- Infected users more than new information, meaning that there is more interaction with tweets containing new information than new information being put out
- Growth for retweets (biggest slope) higher than quote and reply tweets, demonstrating that not all new information keeps spreading

University of Michigan - Ann Arbor

- SIRsim: 94.8% of Susceptible agents, 5% Infected, 0.19% Removed due to death
- SIRemp: 0.002% Infected, minimal Removed cases; higher proportion of Susceptible

INFOcas: 
- Retweet is substantially larger than quote (19x) and reply (16x) cascades
- Retweet has more cascades than quote (23x) and reply (19x) networks 

Correlation
- Infected nodes: highest correlation between SIRsim and INFOcas retweets
- Removed nodes: high correlation between INFOcas retweets and quote tweets; retweets have strongest correlation to quote and reply tweets
- Weak correlations between SIRemp and INFOcas and SIRsim and INFOcas Removed cascades 

COVID‐19 pandemic and information diffusion analysis on Twitter results

Dinh, L., & Parulian, N. (2020). COVID‐19 pandemic and information diffusion analysis on Twitter. Proceedings of the Association for Information Science and Technology, 57(1), e252. https://asistdl.onlinelibrary.wiley.com/doi/full/10.1002/pra2.252?casa_token=QzpH0XtpmkAAAAAA%3ABiTf4V4hwwT8C3b5eR_WOm7EeNOnhIeFvwV-ZLVkslvYfBTnV-qwYUzhVqyoHEWPdL0X9hlluz7W0xcF 

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