# Feature Drift in Fake News Detection:
###### Abstract  
In recent years, fake news detection and its characteristics have attracted a number of researchers. However, most detection algorithms are driven by data rather than theories, which causes the existing approaches to only perform well on specific datasets. To the extreme, several features only perform well on specific datasets. In this study, we first define the feature drift in fake news detection methods, and then demonstrate the existence of feature drift and use interpretable models (i.e., Shapley Additive Explanations and Partial Dependency Plots) to verify the feature drift. Furthermore, by controlling the distribution of tweets' creation times, a novel sampling method is proposed to explain the reason for feature drift. Finally, the Anchors method is used in this paper as a supplementary interpretation to exhibit the potential characteristics of feature drift further. Our work provides deep insights into the temporal patterns of fake news detection, proving that the model's performance is also highly related to the distribution of datasets. feature drift; fake news detection; interpretable analysis +
Footnote †: journal: Medical  
proposed a spreading dynamics model based on modified epidemic diffusion models to study the propagation of fake news. Ref. [14] proposed that echo chambers on social networks lead to the virality of fake news from the perspective of social contagion. In addition, researchers also suggest various algorithms to develop fake news detection methods based on machine learning [19; 20] or deep learning [21; 22; 23; 24]. However, their feature recognition work is driven by data rather than theories [25], which causes the existing approaches to only perform well on specific datasets. Furthermore, most current works focus on the linguistic features [26; 27; 28], which may be deliberately intended for specific purposes, e.g., deceptive language features can be injected to bypass the text-based rumor detection model [29]. Thus, the fragility of linguistic features prompts researchers to start looking for more reliable features, e.g., propagation features (such as propagation range, i.e., the size of the retweet network). Compared to linguistic features, they are more difficult to manipulate by malicious users [30]. Therefore, propagation-based methods [31; 32; 33] have more stable temporal and inter-system validity. However, the basic propagation characteristics and underlying mechanisms of user activity on social media platforms remain poorly understood. In general, different data sources may introduce system biases and yield a lack of stability in the measurement. Thus, exploring underlying mechanisms in fake news detection becomes necessary. Furthermore, the interpretability of deep learning approaches is very poor, e.g., deep neural networks (DNN) [34]. These models, with low interpretability, are challenging to put into practical application, despite the high accuracy that they obtain. Our study demonstrates that the same features perform differently in different datasets. By defining the phenomenon of feature drift in fake news detection as the fact that the features are unstable and fortuitous over a decision process, our work proves the existence of feature drift and discusses the potential reasons. The research questions are as follows:  
* **RQ1**: Does the phenomenon of feature drift exist in fake news detection? * **RQ2**: What causes the feature drift phenomenon? By using SHAP (Shapley Additive Explanations) [35] and PDP (Partial Dependency Plots) [36], we confirm the existence of the feature drift phenomenon. Furthermore, to identify the reason for feature drift, our work proposes a sampling method by controlling the distribution of tweets' creation times. The contributions of this work are as follows:  
* We demonstrate that the feature drift phenomenon does exist in fake news detection, and explainable results further illustrate the existence of feature drift.