## 4 Methodology
### Uni-Modal Representation Extraction
#### 4.1.3 User Module  
**User profile.** User behavior plays an important role in the propagation of the cascade, and the most obvious factor is the user's follower count. The greater the number of followers, the greater the user's influence, and the more widely the post spreads ([PERSON] et al., 2018). Therefore, we also fed users' information into the model, such as how active they are on social media, including their number of followers, their number of friends, whether their account is officially verified, and so on. This information can be seen in the user's attributes via Twitter API, as shown in Table 4 below.  
**User timeline.** The user's posting habits and followers' feedback to the user can also be observed from the user's historical posts. We used the Twitter API to collect the ten tweets preceding the target tweet and observed the user's posting habits from the historical timeline. We extracted two categories of features that shown in Table 5: statistical, such as _avg_time_diff_, which can provide a glimpse into the user's posting habits, and _avg_favorite_count_, which can be used to observe the response of followers. The sentiment feature was the average sentiment score of the timeline acquired through VADER, allowing one to witness whether the content leans towards extremes.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Feature** & **Description** \\ \hline \multirow{6}{*}{Statistical features} & _seq\_enabled_ & Boolean. If true, then the user agrees to have location data posted in his tweets. \\  & _verified_ & Boolean. If true, it indicates that the user has a verified account. \\  & _followers\_count_ & The number of followers this account currently has. \\  & _friends\_count_ & The number of users this account is following. \\  & _listed\_count_ & The number of public lists that this user is a member of. \\  & _statuses\_count_ & The number of tweets (including retweets) that a user has posted. \\  & _favorites\_count_ & The number of tweets this user has marked as favorite in the account’s lifetime. \\ \hline \hline \end{tabular}
\end{table}
Table 4: User profile features.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Feature** & **Description** \\ \hline \multirow{6}{*}{Statistical features} & _user\_count_ & The number of mentioned users in the source tweet. \\  & _tag\_count_ & The number of hashtags in the source tweet. \\  & _symbol\_count_ & The number of symbols in the source tweet. \\  & _url\_count_ & The number of URLs in the source tweet. \\  & _sentence\_count_ & Sentence count of the source tweet. \\ \hline \multirow{6}{*}{Temporal features} & _hour_ & The hour when the source tweet was created. \\  & _weekday_ & Day of the week when the source tweet was created. \\  & _is\_holiday_ & Boolean. When true, the created time of the source tweet is on a holiday. \\ \hline \multirow{6}{*}{Statistical features} & _sentiment\_score_ & The source tweet’s sentiment score. The value is between \(-1\) and \(1\), from the most negative to the most positive. \\  & _pos\_count_ & The number of positive sentences in the source tweet. \\ \cline{1-1}  & _neg\_count_ & The number of negative sentences in the source tweet. \\ \cline{1-1}  & _sentiment\_ratio_ & The ratio of positive sentences to negative sentences. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tweet features.  
In this module, we simply combined all features as a single feature representation, i.e., \(E_{net}\) in Figure 10. This representation was fed into the multi-modal attention fusion module together with the other representations obtained from the other modules.