# Feature Drift in Fake News Detection:
## 4 Feature Drift
### Interpretable Analysis  
Understanding the reasons that news is classified as fake news can better help fact-checkers to make decisions. In this section, we further describe interpretative analyses to investigate feature drift in fake news detection. \begin{table}
\begin{tabular}{c c} \hline \hline
**Structural Features** & **Description** \\ \hline \multirow{2}{*}{AvePath [51]} & Average shortest path. The average value of \\  & the distance of all node pairs in the \\  & propagation graph. \\ \hline \multirow{2}{*}{NoN [49]} & Number of nodes in the propagation graph. \\ \hline \multirow{2}{*}{NoS [49]} & Number of subgraphs in the \\  & propagation graph. \\ \hline \multirow{2}{*}{MaxS [49]} & Tweet number in the largest subgraph. \\  & The number of tweets in the largest subgraph. \\ \hline \multirow{2}{*}{Hgeneity [52]} & Structural heterogeneity, which indicates the \\  & connectivity of the propagation graph. \\ \multicolumn{2}{c}{The expression is as in Equation (3).} \\ \hline \multirow{3}{*}{Motif [53]} & Depth motif degree, which analyzes the graph \\  & structure from the micro perspective. \\ \multicolumn{2}{c}{The expression is as in Equation (4).} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._To better investigate the contributions of features, we first explore feature drift using SHAP. SHAP [35] is a method based on game theory that can construct an additive explanation model and visualize feature contributions. The model generates SHAP values by calculating the difference between predicted values with and without the specific feature value. A larger SHAP value means that the sample is easily classified as positive (here, this represents genuine news) by the corresponding feature. Through violin plots, SHAP can better demonstrate the correlation between the predicted value and the corresponding feature. Since Figure 1 demonstrates that the Random Forest algorithm performs best on both datasets and exhibits a significant feature drift phenomenon, we choose the Random Forest algorithm as the classification model and calculate the SHAP value. Figure 2 shows the SHAP summary plot of Twitter and Gossipcop under different cut-off times, where each dot represents a specific sample, and the color indicates the feature value. For example, if sample A is red in the feature of _Characteristic time (Ctime)_ and the SHAP value is 0.1, this means that if the feature _Characteristic time (Ctime)_ adds to the model (Random Forest), the output result (accuracy) is 0.1 higher than that without this feature, and the _Characteristic time (Ctime)_ value of sample A is large. Figure 1: The performance of four baseline algorithms on the Twitter (**a**) and the Gossipcop (**b**) datasets. As shown in Figure 2, one can find that in different datasets, features have different influences on the output. For example, in Figure 2b, the higher values of _Response time (Rtime)_ and _Number of nodes (NoN)_ correspond to the higher probability of true news. However, in Figure 2d, with the same features, higher feature values lead to a higher probability of fake news. Thus, the same features play different roles in the two datasets, which implies that even if the classifier uses the same feature in different datasets, the results will be very different, especially when the datasets are quite different. Furthermore, it suggests that simply transferring a feature engineering algorithm from one dataset to another is more effective than the other. Figure 2: SHAP summary plots for the Twitter and Gossipcop datasets. (**a**,**b**) refer to the Twitter dataset, (**c**,**d**) refer to the Gossipcop dataset. The _cut-off times_ are (**a**,**c**) 2.4 h and (**b**,**d**) 12 h.  
not feasible in most cases, and fake news detection algorithms are highly dependent on the dataset. In addition, feature drift can exist even within the same dataset if the observation time is different.