case_images: PMC7347154_01
case_images: Degree distribution of the follower networks.
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case_images: PMC7347154_01_pone.0234875.g011.jpg
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case_images: The degree distribution for the network used for the Putin missing event displayed in Fig 11 shows that there are only a few nodes with high in-degree or the number of potential peer signals.
case_images: ABM results for the simulation using Algorithm 1 on three Twitter who-follows-whom network for the events (a) Charlie Hebdo, (b) Putin missing and (c) Ferguson unrest.
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case_images: PMC7347154_01_pone.0234875.g012.jpg
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case_images: Fig 12 show the results of the ABM simulation for the two simulations. Fig 12(a), 12(b) and 12(c) show the plots corresponding to the true diffusion rate over time and it compares the two models: the BM model where the peer influence at time t is characterized by the number of neighbors of a user who have reshared the rumor message till t and the AEM model where the peer influence at time t is characterized by the augmented exposures till t given in Eq 9. Ferguson arrest: For the cascades belonging to the Ferguson arrest event, we fund that in contrast to the results discussed above, the AEM model resulted in slower diffusion than the base model after time step 4 as shown in Fig 12(c). Time Step 4 compared to the BM model which explains the plot Fig 12(c). This suggests that in networked situations unlike our experimental environment, slower initial diffusion can result in initial higher uncertainty which can eventually result in the decay of the influence factor later on.