## 6 Conclusions  
Analyzing user behavior in online communities is an important task. We have presented the user-topic model, which utilizes user participation instead of word occurrence, to analyze online communities in terms of topics. The contributions of our work are summarized as follows: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**\#Topics**} & \multicolumn{2}{c}{**User-Wise**} & \multicolumn{2}{c}{**Topic-Wise**} \\ \cline{3-6}  & & **Proposed** & **Network-Based** & **Proposed** & **Network-Based** \\ \hline \multirow{4}{*}{R1} & 2 & 0.321 & 0.055 & 0.348 & 0.066 \\  & 4 & 0.274 & 0.078 & 0.280 & 0.072 \\  & 8 & 0.203 & 0.071 & 0.203 & 0.052 \\ \hline \multirow{4}{*}{R2} & 2 & 0.323 & 0.045 & 0.344 & 0.056 \\  & 4 & 0.276 & 0.055 & 0.283 & 0.053 \\  & 8 & 0.195 & 0.040 & 0.195 & 0.037 \\ \hline \multirow{4}{*}{SD} & 2 & 0.268 & 0.107 & 0.310 & 0.090 \\  & 4 & 0.256 & 0.092 & 0.267 & 0.092 \\  & 8 & 0.194 & 0.068 & 0.200 & 0.053 \\ \hline \multirow{4}{*}{PG} & 2 & 0.350 & 0.149 & 0.366 & 0.160 \\  & 4 & 0.304 & 0.179 & 0.308 & 0.148 \\ \cline{1-1}  & 8 & 0.227 & 0.097 & 0.225 & 0.089 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Standard deviation of topic probability distribution of users. Figure 9: Topic distributions of articles. The distribution of the proposed method is much wider. * Textual coherence was observed in the measured topics, although the method is language-independent, as we did not use any textual data in the data processing. This showed that users' behavior and interests are reflected in their interactions, especially in common engagement with related articles. * The user-topic model can reflect dynamic features that change over time better than the topic model, which uses only textual data. * Topic flow graphs and temporality measures were shown to be effective in distinguishing thematic and temporal factors in individual topics. * The user-topic model can be used to cluster users, with their behavioral features, from various perspectives such as topical interests and active hours. * The two-stage graph construction and visualization method presented here aids visual analysis of the user interaction network. * Event detection can also be effectively performed with the user-topic model, by analyzing dynamic topic flow. The work presented here is a macroscopic analysis method from the perspective of users and articles, which concerns how topics are distributed, how they change over time, and what significant tendencies we can observe. Future studies should consider a microscopic perspective of the user-topic model, performing behavioral analysis within an individual article. We also plan to extend the proposed method in various directions. One extension would be concerned with the weighting problem in constructing pseudo-documents. In this work, we use the sequence of comment writers, which are viewed as a set of users associated with weights, and the weight of a user is to be the number of comments written by the user in an article. We may apply different functions such as \(\log x\) and \(x^{2}\) to this value and discover the different results that can be obtained with different weighting functions. In addition, many community-specific features exist other than user participation, such as rating, friend relationship, and replying structures. Furthermore, to extend the user-topic modeling method, we need a way to combine these additional features. As a result that one of the aspects we emphasized in this work was simplicity, we also plan to develop a simple method to combine multiple features rather than designing complicated probabilistic models specific to a particular domain. The authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript. This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2017R1D1A1A02018504). The authors declare no conflict of interest.