## 5 Propagation Dynamics in Twitter  
In order to study the propagation dynamics of this content on Twitter, we reconstructed the information cascades for each verified content considering replies, representing 20% of the total interactions triggered by content on Twitter. We released the structural data to favor reproducible research (to access the data, please visit: [[https://github.com/marcelomendoza/disinformation-data](https://github.com/marcelomendoza/disinformation-data)]([https://github.com/marcelomendoza/disinformation-data](https://github.com/marcelomendoza/disinformation-data)), accessed on 10 March 2023). We used the format suggested by [PERSON] et al. [50] to examine cascades in order to create useful data for professionals and researchers in this field. We focused on three characteristics of the cascades: (1) depth, (2) size, and (3) breadth. A first analysis calculated these characteristics on all of the threads added according to veracity. The averages of these characteristics and their deviations are shown in Tables 9-11. Table 9 shows that true content cascades are more shallow than the rest. On the other hand, false contents show trees with a high depth. False and imprecise propagation trees are quite similar. Table 10 shows that false contents reach more people than the rest. This finding corroborates prior results [5], showing that false contents reach more people than other kinds of content. Differences in terms of tree size are significant between the groups. Regarding breadth, Table 11 shows that imprecise propagation trees are wider than the rest. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Feature** & **Range** &
\begin{tabular}{l} **Social** \\ **Out.** \\ \end{tabular} & **COVID-19** & **2021 Elect.** & **Constitution** & **Other** \\ \hline GFOG * & \(\{0,20\}\) & 15.43 & \(\downarrow\) **15.06** & 16.27 & 16.12 & 15.32 \\ NDCRS & \(\{0,20\}\) & 11.12 & 11.18 & 11.64 & 11.54 & 11.35 \\ ARI * & \(\{5,22\}\) & 16.21 & \(\downarrow\) **15.87** & 17.56 & 17.42 & 16.45 \\ FKI * & \(\{0,18\}\) & 12.86 & \(\downarrow\) **10.73** & 12.24 & 12.28 & 11.27 \\ DCRS & \(\{0,20\}\) & 12.64 & 12.98 & 12.46 & 13.82 & 13.65 \\ CLI * & \(\{0,20\}\) & 18.02 & \(\downarrow\) **17.33** & 18.59 & 18.32 & 17.93 \\ LWRI * & \(\{0,100\}\) & 9.11 & 9.74 & 7.23 & \(\downarrow\) **6.98** & 9.32 \\ FRE * & \(\{1,100\}\) & 41.23 & 40.08 & \(\downarrow\) **35.22** & 36.22 & 38.23 \\ IFSZ * & \(\{1,100\}\) & 68.22 & 69.24 & 64.22 & 63.82 & 65.44 \\ FHRI * & \(\{1,100\}\) & 69.23 & 70.02 & 69.67 & \(\downarrow\) **68.23** & 69.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Readability metrics per topic in Twitter. Features marked with * and values in red indicate significant differences. Downward arrows indicate whether the value is lower than that of the other categories. We used one-way ANOVA to compare the means of the five groups at a level of significance = 0.05. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Depth** & **Mean** & **Std** & **Min** & **Max** \\ \hline True & \(\downarrow\) **6.92** & 8.54 & 1 & 78 \\ False & 9.13 & 11.54 & 1 & 88 \\ Imprecise & 9.92 & 10.10 & 1 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Depth of the propagation trees in Twitter (replies). KS-tests: false and true: \(D~{}=~{}0.168\), \(p~{}\sim~{}0.08\), false and imprecise: \(D=0.124\), \(p\sim 0.70\), true and imprecise: \(D=0.275\), \(p\sim 0.01\). Values in red indicate significant differences. Downward arrows indicate whether the value is lower than that of the other categories. We used one-way ANOVA to compare the means of the three groups at a level of significance = 0.05. For a second analysis, this time of a dynamic nature, we calculated the information cascades' characteristics as a time function. In this way, it was possible to assess whether there were differences in the propagation dynamics. Based on this analysis, we focused on the growth patterns of the cascades, considering both the depth, size, and breadth of the propagation trees.