###### Abstract
If more age information is provided through age predictions, more studies on human dynamics need to be considered with the predicted age information. Second, most of the social media data that prior studies used were in English or Chinese, whereas social profiling has rarely been performed using Korean social media data. However, using social media data in various languages for age analysis will provide a richer understanding of various individuals and societies; therefore, efforts to acquire social media data in diverse languages are required for more effective social profiling studies. To resolve the abovementioned problems and challenges, this study selected as its focus one of Korea's major news portal sites, naver.com, and pioneered using it for age information-based social profiling studies. Unlike other sites, naver.com provides age information (i.e., rates of people in their 10s, 20s, 30s, 40s, and \(\geq\)50s as age group distributions) of anonymous commenters on news articles. The age information of naver.com is reliable as users sign into the service via real name authentication. Therefore, its age information can be used for age information-based social profiling studies of social media users in Korea. However, to do so, there are still problems to be solved as below. First, naver.com has a policy of making the age group distribution of anonymous commenters on a news article open to the public only if the number of its direct news comments exceeds a specific number, i.e., 100. (The term \"direct news comment\" has been used for news comments that replied directly to a news article and to distinguish it from \"news comments\" on a news article, which included both direct and indirect replies to news comments). In other words, news articles with fewer than 100 direct news comments can be considered as unlabeled news articles. Around 91% of news articles published on naver.com were found to be unlabeled news articles based on the data that this study collected. Second, as a result, naver.com has not yet been used to predict age information or analyze the social profiles of Korean users by using the age information that has been collected or predicted. Hence, methodologically there are several elements that are unclear: (i) how age information of anonymous commenters on a news article can be represented by extracted features; (ii) which prediction technique can give a better performance at predictingage information using the age information representation; and (iii) how the social profiles of commenters on news articles can be analyzed using the predicted age information. To address these questions, this study proposed a method for predicting the age group distribution of anonymous new commenters for news articles and then using this predicted age information to investigate and understand differences in topics of interest between age groups as human dynamics. To be specific, it adopted a machine learning approach for predicting the age group distribution of anonymous commenters on unlabeled news articles. In this approach, each news article was represented by textual characteristics based on its comments. These labeled news articles were used to evaluate machine learning techniques for predicting age information, and the best prediction technique was selected and used to perform age information predictions for unlabeled news articles. Consequently, all collected news articles could be labeled by age information. Thereafter, using the section information as a cue for topics of interest to age groups, the fuzzy differences of interesting topics among age groups were explored and compared.