# Interaction-Based Behavioral Analysis of Twitter Social Network Accounts
## 6 Conclusions  
In social networks, users can be classified according to their activity and basic descriptive characteristics, and they can also be classified according to their behavior on the network. Users cannot be easily grouped into social networks like in everyday life, and their relationship patterns cannot be derived only by their sharing or descriptive qualities. The interactions and relationships of profiles with multiple communities may differ. However, studies show that there is a similarity between the individual character of a social network user and his or her behavior on social networking environments online. To determine this in the study, behavioral analyses were conducted by investigating the structure of the interaction schemes of online social media users. The classification was successfully performed in accordance with users' characteristics.  
The fact that the data set used is up to date and comprehensive has been one of the main aspects in achieving an effective result with high performance. High performance rates from the most popular data mining algorithms used in classification have determined the accuracy of the methodology applied. The original, comprehensive, and innovative method presented in this study is expected to provide the basis for qualified applications in the field. Our possible future research is to use new machine learning algorithms such as deep learning to more accurately detect spam-bot-malicious accounts.  
Investigation, data collection and filtering, data conversion, software, classification, test, H.I.; Problem definition, methodology, evaluation of obtained results, writing--review and editing, T.T.  
The authors declare no conflict of interest. This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method** & **Accuracy** & **Account Detected** \\ \hline \multirow{3}{*}{[22]} & Random Forest, Artificial Neural & 96.3\% & \multirow{3}{*}{Spm} \\ \cline{3-3}  & Network, and Support Vector Machine & 96\% & \\ \cline{3-3}  & & 95.6\% & \\ \hline
[31] & Random Forest & 86.44\% & Automated, Normal Users \\ \hline \multirow{3}{*}{[32]} & Decision tree, Bayesian Network, Support & \multirow{3}{*}{-90\%} & \multirow{3}{*}{Human, Sybill} \\  & Vector Machine, and Artificial Neural & & \\ \cline{3-3}  & Network & & \\ \hline
[33] & Random Forest & 95\% & Bot, Sybill \\ \hline
[34] & Warped Correlation Method & 94\% & Bot \\ \hline \multirow{3}{*}{[35]} & Logistic Regression, Naive Bayes, & 75\% & \multirow{3}{*}{Bot} \\ \cline{3-3}  & Support Vector Machine, and Gradient & 78\% & \\ \cline{3-3}  & Boosted Trees & 82\% & \\ \cline{3-3}  & & 86\% & \\ \hline
[29] & Naive Bayes and Entropy Minimization & \multirow{3}{*}{90\%} & \multirow{3}{*}{Fake} \\  & Discretization & & \\ \hline
[12] & Random Forest & 96\% & Human, Bot, and Cyborg \\ \hline \multirow{3}{*}{[10]} & Artificial Neural Networks, Decision Tree & 92.08\% & \multirow{3}{*}{Fake} \\  & Classifier, and Random Forest Classifier & 94.58\% & \\ \cline{3-3} \cline{3-3}  & & 95\% & \\ \hline \multirow{3}{*}{**Proposed Method**} & **Support Vector Machine, K-Nearest** & \multirow{3}{*}{**96.81\%**} & **Popular-Active,** \\  & **Neighbors, and Artificial Neural** & & \\ \cline{3-3}  & **Network** & & **Spam-Bot-Malicious** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Methods and success rates of studies in the literature.  
**Data Availability:** Twitter data used in this article can be obtained from the link below. [[https://github.com/hafzulahis/mdpi-applsci-603816](https://github.com/hafzulahis/mdpi-applsci-603816)]([https://github.com/hafzulahis/mdpi-applsci-603816](https://github.com/hafzulahis/mdpi-applsci-603816)).