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This article is cited in 2 scientific papers (total in 2 papers)
Artificial Intelligence, Intelligent Systems, Neural Networks
Automatic extraction of social network users' attitudes on reproductive behavior issues
I. E. Kalabikhinaa, N. V. Lukashevicha, E. P. Baninb, K. V. Alibaevaa, S. M. Rebreyc a Lomonosov Moscow State University
b National Research Center “Kurchatov Institute”
c Moscow State Institute of International Relations (MGIMO)
Abstract:
This paper presents a specialized dataset with annotation of user
attitudes on reproductive behavior. We analyze the features of the “for” and
“against” stance distribution for specific aspects of reproductive behavior. The
created dataset solves two classification problems: classifying messages by
the relevance to a topic being studied and the author’s stance on a particular
issue. We use classical machine learning methods and the BERT-based neural
network classified messages models. The best classification results in both tasks
are achieved based on variants of the BERT model using pairs of sentences
in the classification — variants of NLI (natural language inference) and QA
(question-answering). In addition, the created dataset makes it possible to draw
meaningful conclusions on the attitudes of VKontakte users to reproductive
behavior issues. It was revealed that the phenomenon of deliberate childlessness is
actively represented in VKontakte groups while having many children remains a
poorly widespread model of behavior. Within the framework of the pro-natalist
policy, it is crucial to form a favorable public opinion about parenting, to alleviate
the deficiency of time for parents.
Key words and phrases:
opinion analysis, BERT, supervised learning, demographic policy, VKontakte, reproductive behavior.
Received: 10.11.2021 Accepted: 30.12.2021
Citation:
I. E. Kalabikhina, N. V. Lukashevich, E. P. Banin, K. V. Alibaeva, S. M. Rebrey, “Automatic extraction of social network users' attitudes on reproductive behavior issues”, Program Systems: Theory and Applications, 12:4 (2021), 33–63
Linking options:
https://www.mathnet.ru/eng/ps388 https://www.mathnet.ru/eng/ps/v12/i4/p33
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Abstract page: | 141 | Full-text PDF : | 83 | References: | 31 |
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