Leadership Detection Across Social Media Hashtags
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Дата
2022
ORCID
DOI
Науковий ступінь
Рівень дисертації
Шифр та назва спеціальності
Рада захисту
Установа захисту
Науковий керівник
Члени комітету
Видавець
Анотація
The paper deals with the problem of identifying leaders on the basis of social media hashtags. The relevance of the research topic is determined by the growing interest of employers in employees with developed leadership qualities and the need for clear and accurate detection of leadership abilities without recourse to in-depth psychodiagnostic testing. In this connection, the analysis of the content of social networks of seekers can be a reliable way to identify potential leaders. With a view to studying the specifics of the use of social media hashtags, a group of 214 students (average age 21.4 ± 1.2) studying in various professional fields (technical, humanities, social sciences) was selected, including 109 women and 105 men. The Transformational Leadership Questionnaire was used to study the level of leadership development. To study the attitude to different types of hashtags, we’ve developed a questionnaire aimed at evaluating different types of hashtags. Descriptive statistics and Ttest methods were used to process the study data. The study has found that leaders prefer mixed hashtags, using verbs and calls to action. Not leaders prefer short hashtags with nouns and expressions of their own feelings. The features of the hashtags used by leaders on social media identified in the study can be used in modern professional selection for a prompt and comprehensive assessment of job seekers.
Опис
Ключові слова
leadership, hashtag, social media, self-presentation, self-perception
Бібліографічний опис
Leadership Detection Across Social Media Hashtags [Electronic resource] / O. Romanovskyi [et al.] // CEUR Workshop Proceedings. – 2022. – Vol. 3171. – Computational Linguistics and Intelligent Systems (COLINS 2022) : proc. of the 6th Intern. conf., Gliwice, Poland, May, 2022, 12-13 / ed. V. Lytvyn. – Electron. text data. – Gliwice, 2022. – Vol. 1: Main Conference. – P. 632-641. – URL: https://ceur-ws.org/Vol-3171/paper47.pdf, free (accessed 18.11.2023).