## 6 Conclusions  
Accounts on social media platforms behind which stand nobody but malicious software (commonly known as social bots) have been playing hide-and-seek with researchers in the past decade. Studies have shown that bot detectors' performance on novel social bots is not promising. We believe the crux of the issue lies in the fact that researchers have been constantly playing catch-up with ever-evolving social bots, leading to the development of bot detectors lagging behind. To tackle this, one practical method is to train our own social bots using a virtual environment and reinforcement learning to discover vulnerabilities in advance. By analyzing a large real-world dataset, the heuristic used in previous studies was falsified, and the first bot influence and user engagement models were proposed by analyzing a large real-world dataset. The action space for bots was expanded, and a profile editor module and user preselection mechanism were introduced to simulate advanced bot behaviors. Adversarial experiments were conducted to evaluate whether the proposed bots could  
Figure 7: The density of account for the five most important features (log-transformed and min-max normalized). The green lines represent the corresponding mean values of the bot samples recorded in simulations. -breach RF-based and GNN-based detectors while acting as fake accounts and influuencers, respectively. The results are worrying: the RF-based detector could neither detect them nor avoid their influence propagation. The GNN-based detector, despite outperforming the RF-based detector overall, could still be breached by some bots. The following conclusions can be drawn based on the experimental results. * Aside from the structure of the detector, other information, such as the utilized account data and the output bot score of given accounts, can also be used against it. Social media platforms should think twice before releasing any information relevant to their bot detector. * The impact of evaluation on different aspects of bots is interconnected. A highly developed bot may not necessarily deliver the best performance, and conversely, a bot detector may not effectively detect certain types of bots, even if it can detect bots that are generally considered more advanced. It is advisable to create and test various types of bots to identify vulnerabilities before they can be exploited for malicious purposes. * RF-based detectors may have a higher recall for identifying new social bots compared to GNN-based detectors, although this may come at the expense of precision. It could be worthwhile to conduct further research on using an RF-based detector with a low-pass threshold to screen users before additional analysis or for ensemble learning. * The detectors should undergo separate evaluations using bots with varying account ages, numbers of followers, and number of views. Bots that don't attempt to increase their influence are usually more challenging to identify. Also, it is crucial to discover and suspend influencer bots as soon as possible. This way, they will have fewer followers or views, helping to reduce their impact. Conceptualization, R.J.; methodology, R.J.; software, R.J.; validation, R.J.; formal analysis, R.J.; investigation, R.J.; resources, Y.L. ; data curation, R.J.; writing--original draft preparation, R.J.; writing--review and editing, R.J.; visualization, R.J.; supervision, Y.L. ; project administration, R.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original data presented in the study are openly available in TwiBot-22 at [[https://github.com/LuoUndergradXJTU/TwiBot-22](https://github.com/LuoUndergradXJTU/TwiBot-22)]([https://github.com/LuoUndergradXJTU/TwiBot-22](https://github.com/LuoUndergradXJTU/TwiBot-22)) (accessed on 31 May 2025).