## 4 Results
### Feature Analysis  
Shapley additive explanations (SHAP), introduced in [70], is used to show the contribution or importance of each feature to the prediction of the model. SHAP values analysis, in the case of the RF model, was performed on a sample of 1000 examples from the test set. The absolute SHAP value indicates how much a single feature affected the prediction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Features** & **Acc** & **P** & **R** & **F1** \\ \hline \multirow{3}{*}{RF} & Text & 0.600 & 0.608 & 0.606 & 0.599 \\  & Network & **0.673** & **0.675** & **0.675** & **0.673** \\  & Combined & 0.671 & 0.672 & 0.673 & 0.671 \\ \hline \multirow{3}{*}{MLP} & Text & 0.631 & 0.632 & 0.632 & 0.631 \\  & Network & 0.667 & 0.666 & 0.662 & 0.662 \\  & Combined & **0.679** & **0.678** & **0.678** & **0.678** \\ \hline \multirow{3}{*}{LGBM} & Text & 0.600 & 0.621 & 0.611 & 0.595 \\  & Network & 0.677 & 0.680 & 0.680 & **0.677** \\  & Combined & **0.677** & **0.681** & **0.681** & **0.677** \\ \hline \multirow{3}{*}{CEM} & Text & 0.625 & 0.629 & 0.629 & 0.625 \\  & Network & 0.669 & 0.668 & 0.666 & 0.666 \\  & Combined & **0.680** & **0.679** & **0.679** & **0.679** \\ \hline \multirow{3}{*}{NODE} & Text & 0.615 & 0.624 & 0.621 & 0.613 \\  & Network & 0.667 & 0.667 & 0.661 & 0.661 \\  & Combined & **0.681** & **0.679** & **0.678** & **0.678** \\ \hline \multirow{3}{*}{TabNet} & Text & 0.630 & 0.630 & 0.630 & 0.629 \\  & Network & 0.663 & 0.662 & 0.658 & 0.659 \\ \cline{1-1}  & Combined & **0.669** & **0.667** & **0.666** & **0.666** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of results for six trained models in combination with three different set of features. In order to understand the importance or contribution of the features for the whole dataset, the bee swarm plot is illustrated in Figure 3. In this plot, the features are ordered by their effect on the prediction in such a way that the most important feature is listed on the top, and the rest of the list is sorted in descending order. The features' importance is determined according to SHAP values, which are calculated with a unified framework for interpreting predictions [70] and presented simply with the mean average value for each feature. Features are sorted by the sum of the SHAP value magnitudes across all samples. In addition, the plot also illustrates how higher and lower values of the feature affect the outcome. Small dots on the plot represent a single observation. The horizontal axis represents the SHAP value, whereas the color of the dot shows whether this observation has a higher (red) or lower value (blue) compared to other observations. The features listed in Figure 3 are in order of global importance, with the first feature being the most important and the last being the least important. The most important feature--_log1p_followers_count_--is found to have a very high positive contribution when its values are high, and a very low negative contribution when its values are low. The same applies to the variable _entities.media_, which is second in the order of feature importance. For the third most important feature (_log1p_statuses_count_), high values of the variable were found to make a high negative contribution to prediction, whereas low values made a high positive contribution. Such conclusions can also be drawn from the plot for all other features. Moreover, it can be seen that some features, such as _tweets_keywords_3_out_strength_, hardly (or do not) contribute to prediction, regardless of whether their values are high or low. It is interesting to note how some properties of the network are reflected in features  
Figure 3: SHAP values analysis on bee swarm summary plot illustrating impact on model output. that have a stronger impact than other features that reflect other properties of the network. The most important feature is the number of followers (_1. log1p_followers_count_), and the most important feature from the group of centrality measures is follower in-degree (_4.