## 3 Methodology
### SEBD Architecture and Framework Phases
The multi-view graph attention network model has a more powerful representation of the data, allowing for better accuracy in predictions. The cross-domain evaluation of SEBD demonstrates its ability to effectively identify bot accounts using baseline comparison, a ground truth dataset and machine learning classifiers. SEBD achieved an AUC score of 0.98 using the holdout method and a mean F1 score of 0.96 using cross-validation for the trained model. The results obtained using a random forest classifier with a ground truth dataset support this finding. Additionally, SEBD was more successful in identifying bot accounts compared to the baseline tool. Random forest and ensemble classifiers perform well in the bot detection problem. The SEBD results show that bot accounts participate highly in hashtags on Twitter. Furthermore, the SEBD results using the PAC learning framework indicate that our model meets the required accuracy and confidence levels in regard to the accuracy parameter (epsilon) and the confidence parameter (delta). The testing of different sample sizes shows that our results satisfy the PAC learning conditions for sample sizes > 2000 in terms of FNRs and FPRs. Additionally, the empirical error and true error show that the model is able to effectively generalize to unseen data. Graph neural network models are powerful tools to extract hidden knowledge for classification tasks. Therefore, future studies should address looking for bot masters, as these bot accounts work as members of a botnet. In future work, SEBD will be examined using a trending hashtag (one active hashtag); an evaluation will be performed using one active hashtag over a longer period of time (e.g., days) to evaluate changes in predictions, and it will include concept drifts and window sliding. Conceptualization, E.A. ; Methodology, E.A. ; Software, E.A. ; Formal analysis, E.A. ; Data curation, E.A. ; Writing--original draft, E.A. ; Writing--review and editing, E.A., K.H. and H.A. ; Visualization, E.A. ; Supervision, K.H. and H.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The used data as benchmark dataset is from [54]. For our dataset please contact us ([EMAIL_ADDRESS]). The authors declare no conflict of interest. Figure 7: Visualization of SEBD results using t-SNE. ## References

* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] Is this the era of misinformation yet: Combining social bots and fake news to deceive the masses. In Proceedings of the Web Conference 2018, Lyon, France, 23-27 April 2018; pp. 1557-1561. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Malicious accounts: Dark of the social networks. _J. Netw. Comput. Appl_**2017**, _79_, 41-67. [CrossRef]
* [PERSON] and [PERSON] (2022) [PERSON]; [PERSON] Characterizing the role of bots' in polarized stance on social media. _Soc. Netw. Anal. Min._**2022**, _12_, 1-24. [CrossRef] [PubMed]
* [PERSON] and [PERSON] (2021) [PERSON]; [PERSON]; [PERSON] A comparative study of Bot Detection techniques methods with an application related to COVID-19 discourse on Twitter. _arXiv_**2021**, arXiv:2102.01148. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] DEBATENIGHT: The role and influence of socialbots on twitter during the first 2016 U.S. presidential debate. In Proceedings of the 12 th International AAAI Conference on Web and Social Media, ICWSM 2018, Palo Alto, CA, USA, 25-28 June 2018; pp. 300-309. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] Polarization and acculturation in US Election 2016 outcomes--Can twitter analytics predict changes in voting preferences. _Technol. Forecast. Soc. Change_**2019**, _145_, 438-460. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Cashtag piggybacking: Uncovering spam and bot activity in stock microblogs on Twitter. _ACM Trans. Web._**2019**, _13_, 11. [CrossRef]
* [PERSON] et al.