### Reviews on Related Works  
User profiling refers to the process of collecting, cleaning, and presenting an individual's characteristics that are related to demographics and behavior [17]. These attributes often include basic information (e.g., age, gender, location, education, and occupation). Additionally, user profiles can include elements that reflect more complex aspects, such as preferences, interests, behaviors, and personality traits. Recently, user profiling has evolved into social profiling, which leverages social information to generate user profiles (e.g., social actions, such as clicks and likes on social media) [18]. Prior social profiling studies are summarized in Table 1.  
As shown in Table 1, social profiling can be classified into individual and group profiling [3]. Individual profiling involves learning about a person based on demographics (e.g., age, gender, and location) and psychographics (e.g., behavior, personality traits, and interests) by directly asking questions or tracking behaviors online and offline. Group profiling is a process used to represent individuals who share common attributes and may or may not be identified as a group. It mainly includes community detection and subsequent analysis of communities. In relation to the taxonomy of social profiling, this study focused on age, one of the demographic characteristics of individual profiling, but extended to the perspective of group profiling by using the distribution of age groups among commenters on a news article. According to this study's purpose, prior works related to social profiling can be divided into three categories: predicting social profile attributes (also known as digital footprints) [19], using collected or generated social profiles to analyze human dynamics, and performing both in a sequence. However, most of the prior studies aimed at predicting the attributes of social profiles (e.g., personality traits and demographics). Based on the taxonomies in Table 1, this study's purpose can be classified as examining both age prediction and human dynamics, using the predicted age information and interests of groups as two social profile attributes. As age differences were analyzed at different topic levels using section information, this study contributes to the related literature. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Previous Work**} & \multirow{2}{*}{**Description**} & \multicolumn{2}{c}{**Types of Social Profile Attributes**} & \multicolumn{2}{c}{**Types of Purpose**} \\ \cline{3-6}  & & **Individual** & **Group** & **Prediction** & **Human Dynamics Studies** \\ \hline [PERSON] and [PERSON] [20] & Personality traits prediction & Personality traits & & \(\surd\) & \\ \hline [PERSON], [PERSON], and [PERSON] [17] & Personality traits prediction & Personality traits & Personality traits & \(\surd\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Prior social profiling studies. Table 1 shows that most of the prior studies focused on predicting social profiles. Table 2 provides additional details on the prior studies in terms of their machine learning approaches. The findings emerging from Table 2 are as follows:  
First, representative social media, such as Twitter, Facebook, Instagram, and Weibo were used as data sources, and most of the text data was in English or Chinese. Second, various features were used as footprints on social media, representing target social profile attributes. They could be grouped into text, images, social relations, and social behaviors, whereas the type of footprint used on social media depended on the types of data sources (e.g., text or social features for Twitter and Weibo, and images for Flickr and Instagram). Third, for the type of machine learning, supervised learning (e.g., classification and regression) was mostly adopted. Prediction techniques varied from traditional machine learning techniques (e.g., support vector machine (SVM) and multilayer perceptron (MLP)) to the