### Reviews on Related Works
\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** & \begin{tabular}{c} **Human Dynamics** \\ **Studies** \\ \end{tabular} \\ \hline [PERSON] et al. [21] & \begin{tabular}{c} Joint gender and age \\ prediction \\ \end{tabular} & Gender, Age & & \(\surd\) & \\ \hline [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [11] & \begin{tabular}{c} Age group classification of \\ people in their 10s to \\ adults, analyzing the \\ importance of age \\ information to the \\ precision of a sentiment \\ metric \\ \end{tabular} & Age, sentiment, and relationship & \(\surd\) & \(\surd\) \\ \hline [PERSON] et al. [22] & Demographics prediction & \begin{tabular}{c} Gender, Age, and \\ Location \\ \end{tabular} & \(\surd\) & \\ \hline [PERSON] et al. [23] & Age prediction & Age & \(\surd\) & \\ \hline [PERSON] and [PERSON] [24] & Gender difference analysis & \multicolumn{2}{c}{ Gender, Interest} & \(\surd\) \\ \hline [PERSON] et al. [25] & \begin{tabular}{c} Prediction of age and \\ gender \\ \end{tabular} & Age, Gender & \(\surd\) & \\ \hline [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [7] & \begin{tabular}{c} Prediction of age, gender, \\ and personality traits \\ \end{tabular} & \begin{tabular}{c} Age, Gender, \\ Personality traits \\ \end{tabular} & \(\surd\) & \\ \hline [PERSON] et al. [26] & \begin{tabular}{c} Personality traits \\ prediction \\ \end{tabular} & Personality traits & Personality traits & \(\surd\) & \\ \hline [PERSON] et al. [27] & Age Prediction & Age & Age & \(\surd\) & \\ \hline [PERSON], [PERSON], and [PERSON] [15] & Age Prediction & Age & Age & \(\surd\) & \\ \hline [PERSON] and [PERSON] [28] & Personality traits & Personality traits & & \(\surd\) & \\ \hline [PERSON] et al. [29] & \begin{tabular}{c} Personality traits \\ prediction \\ \end{tabular} & Personality traits & Personality & \(\surd\) & \\ \hline [PERSON] et al. [30] & \
\begin{tabular}{c} Personality traits \\ prediction \\ \end{tabular} & Personality traits & & \(\surd\) & \\ \hline This study &
\begin{tabular}{c} Age prediction and fuzzy \\ age differences in terms of \\ interesting topics \\ \end{tabular} & Age, Interest & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._recent deep learning techniques (e.g., convolution neural network (CNN) and recurrent neural network (RNN)). Compared to the previous studies summarized in Table 2, this study used news articles and their comments from the Korean social media website www.naver.com, which has rarely been studied because of anonymity and incompleteness of its social profile data. Regarding the type of footprint used in social media, this study generated and used word embeddings from text to represent age groups (i.e., the distribution of commenters on a news article by age group). In particular, it used the word2 vec approach [31] for word embeddings to overcome difficulties in text representation (e.g., news comments are very short, although their lengths are varied, and many of them are grammatically incorrect, either accidentally or intentionally). Regarding the prediction techniques in Table 2, although there are more complicated machine learning models, deep learning models such as CNN or RNN may achieve better performance than classical models in the case of massive training datasets. However, the scale of a personality dataset is usually small owing to the high cost of collecting sample data, and classical models can also achieve comparable results for such a small dataset [26]. Consequently, this study did not involve deep learning models. Instead, it focused on regression tasks for its unsupervised learning-based approach (i.e., predicting the distribution of age groups for a news article in unlabeled datasets, whose size was much larger than labeled datasets).