Identifying Reproductive Behavior Arguments in Social Media Content Users’ Opinions through Natural Language Processing Techniques

Abstract

Big data provides researchers with valuable sources of information for studying demographic behavior in the population. One such source is the texts posted by social network users on various demographic issues. This study utilizes methods for automatically extracting user opinions from the “VKontakte” social network. The extracted texts are then classified using the Conversational RuBERT neural network model to investigate opinions related to reproductive behavior in the population. The classification process addresses two consecutive problems. Firstly, it aims to identify whether a user’s comment contains argumentation. Secondly, if an argument is present, it seeks to determine its type within the context of the “personal-public” dichotomy. To search for arguments and classify their types, six experiments were conducted, varying the dataset and the number of classes. The method employed for automatic extraction and classification of user opinions on the “VKontakte” social network has demonstrated the ability to accurately classify users’ comments, identifying the presence of argumentation and categorizing the arguments within the “personal-public” dichotomy. This enables the identification of personal and social attitudes, values, stories, and opinions, thus facilitating the study of reproductive behavior.