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Multi-Label Prediction-Based Fuzzy Age Difference Analysis for Social Profiling of Anonymous Social Mediaopen access

Authors
Suh, Jong Hwan
Issue Date
Jan-2024
Publisher
MDPI
Keywords
age predictions; anonymous news commenters; word embedding; machine learning; age difference analysis; fuzzy sets
Citation
APPLIED SCIENCES-BASEL, v.14, no.2
Indexed
SCIE
Journal Title
APPLIED SCIENCES-BASEL
Volume
14
Number
2
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/69649
DOI
10.3390/app14020790
ISSN
2076-3417
Abstract
Age is an essential piece of demographic information for social profiling, as different social and behavioral characteristics are age-related. To acquire age information, most of the previously conducted social profiling studies have predicted age information. However, age predictions in social profiling have been very limited, because it is difficult or impossible to obtain age information from social media. Moreover, age-prediction results have rarely been used to study human dynamics. In these circumstances, this study focused on naver.com, a nationwide social media website in Korea. Although the social profiles of news commenters on naver.com can be analyzed and used, the age information is incomplete (i.e., partially open to the public) owing to anonymity and privacy protection policies. Therefore, no prior research has used naver.com for age predictions or subsequent analyses based on the predicted age information. To address this research gap, this study proposes a method that uses a machine learning approach to predict the age information of anonymous commenters on unlabeled (i.e., with age information hidden) news articles on naver.com. Furthermore, the predicted age information was fused with the section information of the collected news articles, and fuzzy differences between age groups were analyzed for topics of interest, using the proposed correlation-similarity matrix and fuzzy sets of age differences. Thus, differentiated from the previous social profiling studies, this study expands the literature on social profiling and human dynamics studies. Consequently, it revealed differences between age groups from anonymous and incomplete Korean social media that can help in understanding age differences and ease related intergenerational conflicts to help reach a sustainable South Korea.
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Suh, Jong Hwan
경영대학 (경영정보학과)
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