# Spam Classification Based on Signed Network Analysis
## 6 Conclusions and Future Work  
In this paper, we present a spam classification method based on the structural balance theory and social status theory. Our key hypothesis is that the edge signs are highly likely to be determined by considering users' social relationships, so there will be a substantial difference between the edge sign patterns of spammers and those of non-spammers. Through Twitter data analysis, we can observe the apparent difference for spammers and non-spammers in terms of surprise, and we show that the proposed scheme obtains significant classification performance improvement compared to conventional schemes. It achieves an accuracy of 93%, a precision of 95%, and a recall of 91%. In addition, to apply our scheme to unsigned networks, we develop a graph-converting method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rank** & **Information Gain** & **Feature** & **Rank** & **Information Gain** & **Feature** \\ \hline
1 & 0.639 & ST9 & 11 & 0.522 & BT2 \\
2 & 0.613 & ST1 & 12 & 0.507 & ST8 \\
3 & 0.609 & BT3 & 13 & 0.436 & ST14 \\
4 & 0.605 & T11 & 14 & 0.411 & ST2 \\
5 & 0.603 & ST3 & 15 & 0.363 & BT0 \\
6 & 0.584 & BT1 & 16 & 0.348 & ST10 \\
7 & 0.573 & ST5 & 17 & 0.339 & ST13 \\
8 & 0.560 & ST12 & 18 & 0.311 & ST15 \\
9 & 0.552 & ST7 & 19 & 0.251 & ST6 \\
10 & 0.551 & ST4 & 20 & 0.211 & ST16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Information gain in the decision tree.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **FPR** & **F1-Score** \\ \hline SybilRank & 0.283 & 0.303 & 0.335 & 0.769 & 0.318 \\ NFS & 0.572 & 0.597 & 0.443 & 0.299 & 0.509 \\ CatchSync & 0.903 & 0.894 & 0.915 & 0.109 & 0.904 \\ TSP & 0.908 & 0.906 & 0.911 & 0.095 & 0.908 \\ Balance & 0.925 & 0.943 & 0.904 & 0.055 & 0.923 \\ Status & 0.929 & 0.949 & 0.907 & 0.049 & 0.927 \\ Balance + Status & 0.928 & 0.948 & 0.906 & 0.050 & 0.926 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison result.  
From the analysis of the Epinions dataset, we discover the fact that the signs of edges have strong correlations with PageRank and Conductance scores, and based on that, we assign positive and negative signs to the edges. Extensive real-world dataset experiments verify that the proposed converting method works well for unsigned networks.  
We believe that we can not only further improve the performance of the proposed scheme, but also broaden the scope of it to a variety of applications that can be modeled as signed networks. For example, in recommendation systems, the sign of an edge might be determined based on ratings. In social blogging or community sites, sentiment analysis of users' reviews and comments might be conducted for identifying attributes of nodes and links. The proposed scheme is basically orthogonal to conventional behavior-based or content-based spam detection technologies, and thus, they both complement each other. In particular, advanced classifiers with graph-embedding methods could be employed for achieving better classification performance. In the case that the given network data lack information about node and edge attributes, node and edge prediction methods through semi-supervised learning could be applied. We leave the further study of these issues for future work.  
Conceptualization, S.J.; methodology, S.J.; writing--review and editing, S.J. and K.-h.L.; funding acquisition, K.-h.L. All authors have read and agreed to the published version of the manuscript.  
The present research was supported by the research fund of Dankook University in 2020.  
The authors declare no conflict of interest.