## 4 Conclusions  
Social profiling research requires demographic information, of which age information is essential. Previous studies have revealed that differences exist in social and behavioral characteristics, according to age. It has also been found that such age differences are prominent on specific topics. Therefore, to acquire such age information for social profiling, prior studies have primarily focused on predicting age information on social media. However, age predictions on social media have been limited because it is difficult or impossible to obtain age information from social media owing to its anonymity and privacy policies. Moreover, these predictions have focused mainly on English and Chinese social media and are yet to be connected to studying human dynamics. In using the acquired data of this study to fill those research gaps, the method of how age information can be represented and best predicted was also not clearly defined. To address these problems, this study proposed a method that predicts age information from naver.com after selecting the best prediction technique and used the predicted age information for difference analysis between age groups regarding topics of interest. As regards the prediction techniques of machine learning, it evaluated eight models with comparisons, using labeled datasets. Consequently, the multi-label regression of SVM, i.e., SVRmulti, gave the best results for age prediction with labeled data, showing better performance than the other prediction techniques in a statistically significant manner. Hence, it was used to predict the age information of the unlabeled data, which were mixed with the labeled dataset, resulting in a mixed dataset. Subsequently, when general statistical approaches were employed to compare the two datasets, this study found differences between labeled and mixed datasets. Similarities existed between the two datasets when they were compared using a different method (i.e., ranks for an age group \(g\) within a subsection s). Thus, even though age information between labeled and mixed datasets could not be considered strictly identical, age prediction results of the unlabeled dataset still need to be included and used for social profiling. Moreover, using the predicted age information, this study performed a fuzzy difference analysis between age groups with respect to their topics of interest, using the section information of the collected news articles. The highlights of the obtained difference analysis results are as follows. The measured similarities between age groups showed that the age difference in terms of topics of interest was small in (20s, 30s) and (30s, 40s), whereas it was big in (10s, \(\geq\)50s). Besides correlations between age groups, this study also showed that the difference in terms of topic correlations was small in (10s, 20s) and (20s, 30s), but it was significant in (30s, \(\geq\)50s). When uncertainty was considered using the correlation-similarity matrix and defining the fuzzy sets of age difference, the difference in (10s, 20s), (20s, 30s), and (40s, \(\geq\)50s) was shown to be small, while (10s, 40s), (10s, \(\geq\)50s), (20s, \(\geq\)50s) and (30s, \(\geq\)50s) showed obvious distinctions between age groups. As such, this study was able to uncover differences in topics of interest between age groups which may have caused intergenerational conflicts and social problems between age groups in Korean society. However, it is necessary to be cautious in determining differences between generations based on the results of this study, and more in-depth studies should be conducted. This is because different conclusions can be reached depending on the type or quality of data acquired. In addition, this study assumed that people in the same age group have similar topics of interest due to the nature of the data used, but it should be