Attribution of Emotions as an «Image of Old Age» Component According to the Predicate Analysis of YouTube Discussions

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Abstract

The article examines the issues of stereotypes of older age exist in the public consciousness and manifest in the discourse. Based on the 2048 discussions in the Russian YouTube not directly related to age, the implicit components of age stereotypes are studied using a new text mining method, the emotive-predicate analysis. The method allows us to identify automatically the text descriptions of an emotional reactions: to qualify the emotion itself, and to determine its subject and its cause. The frequency of lexemes that name emotions and their types, as well as the subjects and causes of emotions, is considered as an indicator of the significance of the corresponding concepts in the emotional worldview of the author. This approach revealed that the reduced ability to emotional experience and especially emotional influence is stereotypically attributed to older people. An attributing emotionality to older men to a greater extent than to older women manifests a specific deformation of gender emotional stereotypes. The obtained data can be used to work out and improve programs for gerontological education and prevention of gerontological ageism for a professional train in the modern world.

General Information

Keywords: age stereotypes, ageism, network discussions, text mining, predicate analysis, emotives

Publication rubric: Internet and Risks of Communication in the Digital Environment

Article type: scientific article

Funding. The reported study was funded by Russian Foundation for Ba¬sic Research (RFBR), project number 18–29–22047мк.

For citation: Kuznetsova Y.M. Attribution of Emotions as an «Image of Old Age» Component According to the Predicate Analysis of YouTube Discussions. Digital Humanities and Technology in Education (DHTE 2021): Collection of Articles of the II All-Russian Scientific and Practical Conference with International Participation. November 11-12, 2021 / V.V. Rubtsov, M.G. Sorokova, N.P. Radchikova (Eds). Moscow: Publishing house MSUPE, 2021., pp. 509–520.

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Information About the Authors

Yuliya M. Kuznetsova, PhD in Psychology, Senior Researcher, Federal Research Center ‘Computer Science and Control’ of the Russian Academy of Sciences, Moscow, Russia, ORCID: https://orcid.org/0000-0001-9380-4478, e-mail: kuzjum@yandex.ru

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