# Feature Drift in Fake News Detection:
## 4 Feature Drift
### Interpretable Analysis
For example, in the Gossipcop dataset, features _Network diameter (Diameter)_ and _Tweet number in largest subgraph (MaxS)_ perform poorly in the early stages of propagation (_cut-off time_ equals 2.4 h) as the SHAP values converge at around 0. However, they perform well when the _cut-off time_ is 12 h, as the SHAP values are regularly distributed on the horizontal axes, as shown in Figure 2c,d. In summary, features play different roles in different datasets and with different observation times, which means that the transfer effect of the fake news detection algorithms is usually unsatisfactory. The SHAP summary plot gives each feature's contribution to the final prediction, such as whether a high feature value corresponds to a high possibility of true or fake news, and which features are more effective in detecting fake news. However, abnormal samples in SHAP values may affect the classification results, and SHAP values are unable to identify the specific relationship between the feature values and the prediction results. For example, in Figure 2c, it is difficult to quantify how large the value of _Average shortest path (AvePath)_ must be in order to lead to the classification of true news. In order to clearly exhibit the impact of a single feature on the performance of detection methods and understand the relationship between classification results and feature values, we use PDP [36] to show the marginal effect of a feature on the classification results. Figure 3 shows the PDP of the feature of _Average shortest path (AvePath)_ in Gossipcop with the _cut-off times_ of 2.4 h and 12 h. The figure demonstrates a representative partial dependence of the predicted value as the _Average shortest path (AvePath)_ feature value increases. As shown in Figure 3, the feature's tendency is significantly different under the two _cut-off times_. In the case of a _cut-off time_ equal to 2.4 h, with the increase in the _Average shortest path (AvePath)_, this feature significantly impacts the classification results (Figure 3a). Contrastingly, in the case of a _cut-off time_ equal to 12 h, the feature of _Average shortest path (AvePath)_ has no impact on the results (Figure 3b). In more detail, Figure 3a implies that the larger the value of _Average shortest path (AvePath)_ is, the more likely the sample is to be true news. Furthermore, the critical point is around 1.9, which can provide the decision boundary of the detection algorithm. The difference in PDP between the two _cut-off times_ indicates that the feature drift phenomenon in the Gossipcop dataset is significant, and this result is consistent with the conclusion illustrated in Figure 2c,d, i.e., the feature's trend in PDP corresponds to the SHAP value. In summary, the feature drift phenomenon could be observed by using interpretable models such as SHAP and PDP (RQ1). However, the different results on Gossipcop and Twitter prompt us to investigate the reason for feature drift (RQ2). To understand how feature drift differs, we compare the tweets' _creation times_ in the two datasets; as shown in Figure 4, the distributions of the _creation time_ under the two datasets are different. Specifically, _creation time_ in the Twitter dataset is more concentrated in the early stage (0-2 h), with few new tweets participating in the later propagation. Conversely, the _creation time_ in the Gossipcop dataset is more evenly distributed, with more tweets participating in the later stages of propagation (6-12 h). The difference in the distribution of the two datasets regarding the _creation time_ may lead to feature drift. To better understand the role of tweets' _creation times_ in feature drift, we propose a matching method to control the distribution of tweets' _creation times_ when studying the feature drift. In more detail, to measure the asymmetry of events' _creation time_ distribution, we calculate the skewness of each event's distribution.