## 4 Results
### Fake Account Simulation  
The bots acted as fake accounts in these experiments and focused on evading detection. The final survival rate and average bot score are presented in Table 1. The RF detector was unable to effectively detect all variations of the bot strategy model, with the lowest survival rate being 81.8%. The proposed improvements for the bots were overall beneficial, either increasing the survival rate or lowering the average bot score. The best results were achieved using the combined bot strategy, resulting in a survival rate of 99.0% and an average bot score of 0.354. These results are 12.3% and 0.056 better than the baseline, respectively. On the other hand, BotRGCN has achieved more promising results compared to the RF detector. The bots' survival rates were significantly lower overall. Still, the additional actions bot strategy model managed to evade its detection with a survival rate of 85.9%. The additional actions model achieved an average bot score of 0.115, indicating that it is challenging for the detector to identify them by reducing the threshold. Surprisingly, introducing the profile editor and the preselection primarily decreased the survival rate. During the initial stages of the simulations, frequent profile updates were observed when employing the bot strategy with the profile editor. Similarly, a more significant rise in the number of followers was noticed in the simulations utilizing preselection. The observation that abstaining from certain actions might have yielded better outcomes (yet the bot strategy model failed to recognize this) implies that while such actions offer short-term rewards, and they could be detrimental in the long run, disturbing the training of the bot strategy model. Moreover, this demonstrates that bots do not need to be 'advanced' to avoid detection. By experimenting with different bot strategies, one can uncover vulnerabilities in the detectors. The number of undiscovered bots and the average bot score throughout the experiments are presented in Figure 4. Figure 4a,b show that all bots discovered by the RF detector were detected in the first detection. BotRGCN also discovered more bots at the early stage of the simulation but kept detecting bots throughout the experiments. The reason for this can be found in Figure 4c,d. Despite the RF detector being a discontinuous function, the average bot scores throughout the simulation were much more steady than those given by BotRGCN, demonstrating that BotRGCN can perceive slighter changes in bot states. The latest studies increasingly focus on deep learning and using GNN to create social bot detectors. Our findings above also support that GNN-based bot detectors are more advanced than traditional RF detectors. However, the average bot scores tell a different story. The results indicate that the highest and lowest average bot scores achieved with the RF detector were 0.410 and 0.352, respectively, while those achieved with BotRGCN were significantly lower at 0.115 and 0.028. Figure 5a,b display the distribution of bot scores for human accounts given by the RF detector and BotRGCN. When using the RF detector, 10.0% of the human accounts were misclassified as bots with a threshold of 0.5. This misclassification rate would increase to 16.0% and 21.5% with threshold values of 0.410 and 0.352, respectively. If using BotRGCN, the misclassification rate would jump from 9.2% to 42.7% and then to 79.7% as the threshold was lowered from 0.5 to 0.115 and 0.028, respectively. Therefore, we argue that we cannot jump to the conclusion that GNN-based detectors are more advanced. Traditional RF-based