(Non)smoking comments classified by arguments, gender and age
Authors/Creators
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1.
Lomonosov Moscow State University
Description
Data collection. Methods of classification
The comments were collected by the authors during March-August 2024 from the most popular YouTube videos, in which the topic of smoking was discussed in Russian.
When selecting videos, the main criteria were the following: 1) relevance of the video title (the topic of smoking); 2) language (Russian); 3) popularity (number of views). Our goal was to collect all of the most popular videos, since they involved a large number of people in discussions. The search for relevant videos was carried out directly on the YouTube platform. Finally, we collected 204 videos (see Sheet ‘YouTube Video 204’).
The final database includes more than 165 thousand comments (see Sheet ‘All Comments 165th’). Sentiment classification was made by Romanov’s method (Romanov A.S. Methodology for identifying the author of text information for solving cybersecurity problems. Abstract of the dissertation for the degree of Doctor of Technical Sciences. Tomsk, 2024 (Романов А.С. Методология идентификации автора текстовой информации для решения задач кибербезопасности. Автореферат диссертации на соискание ученой степени доктора технических наук. Томск, 2024)).
Using generative artificial intelligence LLM gemma2-9b-it, we classified more than 58 thousand comments on the presence and type of argument to quit smoking or not to quit smoking (see Sheet ‘Argument 58ths’). Sample 58ths from 165ths comments is the sample from the most populated videos.
For more information on the classification of arguments, see Kalabikhina, I.E., Kazbekova, Z.G., & Zubova, E.A. (2024). Arguments of social media users regarding quitting smoking (based on machine learning methods). Management Issues, 18(5), 48–67 (Калабихина, И.Е., Казбекова, З.Г., & Зубова, Е.А. (2024). Доводы пользователей социальных медиа по поводу отказа от табакокурения (на основе методов машинного обучения). Вопросы управления, 18 (5), 48–67).
Finally, on the basis of generative artificial intelligence LLM gemma2-9b-it we classified the comments that contained an argument to quit smoking or not to quit smoking according to gender and age of the comment author. This sample consists of 5.5 thousand of classified comments with argument (see Sheet ‘Gender&Age 5.5ths’).
Promt Example: The best Promt for gender classification (84%, in Russian): Определи пол авторов комментариев. Представь результаты в виде таблицы из 2 столбцов: первый - объяснение выбора, второй - пол автора (или мужской, или женский, или невозможно определить). В первую очередь обращай внимание на окончания глаголов, указывающие на принадлежность к мужскому или женскому полу. Пример: "Я сделала это" - автор этого комментария женщина, это видно по форме глагола. "Я сделал это" - автор этого комментария - мужчина.
Data format and structure
The database includes data on (non)smoking comments in .xls formats.
The variable ‘Sentiment’ includes the following values (see Sheet ‘All Comments 165th’):
“NEGATIVE”
“neutral”
“POSITIVE”
The variable ‘Argument type’ includes the following values (see Sheet ‘Argument 58ths’):
“1” – A comment does not contain an argument to quit smoking or an argument not to quit smoking.
“2” – A comment contains an argument to quit smoking due to the harm it causes to the smoker's health.
“3” – A comment contains an argument to quit smoking due the high cost of cigarettes.
“4” – A comment contains an argument to quit smoking for reasons other than caring for own health and saving money.
“5” – A comment contains an argument not to quit smoking due to fear of gaining excess weight.
“6” – A comment contains an argument not to quit smoking for reasons other than fear of gaining excess weight.
“0” – Classification error.
The variables ‘gender’ and ‘age’ include the following values (see Sheet ‘Gender&Age 5.5ths’):
The variable ‘gender’ includes the following values:
“1” – A comment was written by a man.
“2” – A comment was written by a woman.
“3” – The author’s gender cannot be identified.
“0” – Classification error.
The variable ‘age’ includes the following values:
“1” – A comment was written by a person under 18 years old.
“2” – A comment was written by a person aged 19-34.
“3” – A comment was written by a person aged 35+.
“0” – Classification error.
Other (English)
Supplement: Our publications on digital demography
Original articles:
1. Калабихина, И. Е., Мошкин, В. С., Колотуша, А. В. & др. Анализ отзывов пациентов с использованием машинного обучения и лингвистических методов (2025). Онтология проектирования, 15 (1), 55–66.
2. Kalabikhina, I., Moshkin, V., Kolotusha, A., Kashin, M., Klimenko, G., & Kazbekova, Z. (2024). Advancing Semantic Classification: A Comprehensive Examination of Machine Learning Techniques in Analyzing Russian-Language Patient Reviews. Mathematics, 12, 566.
3. Калабихина, И.Е., Казбекова, З.Г., & Зубова, Е.А. (2024). Доводы пользователей социальных медиа по поводу отказа от табакокурения (на основе методов машинного обучения). Вопросы управления, 18 (5), 48–67.
4. Kalabikhina, I., Zubova, E., Loukachevitch, N., Kolotusha, A., Kazbekova, Z., Banin, E., & Klimenko, G. (2023). Identifying Reproductive Behavior Arguments in Social Media Content Users. Opinions through Natural Language Processing Techniques, Population and Economics, 7(2), 40-59.
5. Калабихина, И. Е., Казбекова, З. Г., Банин, Е. П., & Клименко, Г. А. (2023 b). Демографические ценности и социально-демографический портрет пользователей ВКонтакте: есть ли связь? Вестник Московского университета. Серия 6. Экономика, 3, 157–180.
6. Калабихина, И. Е., Лукашевич, Н. В., Банин, Е. П., & Алибаева, К. В. (2022 а). Автоматический анализ репродуктивных ценностей пользователей сети ВКонтакте. Интеллектуальные системы. Теория и приложения, 26(1), 90–96.
7. Калабихина, И. Е., Казбекова, З. Г., Клименко, Г. А., & Колотуша, А. В. (2022 b). Демографический рейтинг регионов по активности публикаций СМИ о материнском (семейном) капитале. Прикладная эконометрика, 67, 46–73.
8. Калабихина, И. Е., Лукашевич, Н. В., Банин, Е. П., Алибаева, К. В., & Ребрей, С. М. (2021). Автоматическое извлечение мнений пользователей социальных сетей по вопросам репродуктивного поведения. Программные системы: теория и приложения, 12(4(51)), 33–63.
9. Kalabikhina, I.E.; Banin, E.P.; Abduselimova, I.A.; Klimenko, G.A.; & Kolotusha, A.V. (2021a). The Measurement of Demographic Temperature Using the Sentiment Analysis of Data from the Social Network VKontakte. Mathematics, 9, 987.
10. Калабихина, И.Е., Банин, Е.П., Абдуселимова, И.А., & др. (2020). Краткосрочное прогнозирование демографических тенденций на основе данных Google trends. Прикладная информатика, 15(6), 91-118.
Open datasets:
11. Kalabikhina, I. E., & Banin, E. P. (2021). Database “Childfree (antinatalist) communities in the social network VKontakte”. Population and Economics, 5(2), 92-96.
12. Kalabikhina, I. E., Klimenko, G. A., Banin, E. P., Vorobyeva, E., & Lameeva, A. D. (2021b). Database of digital media publications on maternal (family) capital in Russia in 2006–2019. Population and Economics, 5(4), 21-29.
13. Kalabikhina, I. E., Loukachevitch, N. V., Banin, E. P., Alibaeva, K. V., & Rebrey, S. M. (2021c). Automatic extraction of opinions of users of social networks on reproductive behavior issues [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5561126
14. Kalabikhina, I. E., & Banin, E. P. (2020). Database “Pro-family (pronatalist) communities in the social network VKontakte”. Population and Economics, 4(3), 98-103.
Thesis and Chapters:
15. Kalabikhina I., N. Loukachevitch, E. Banin, & A. Kolotusha (2024). Text as data in demography: Russian-language experience. Chapter 14. In: Population and Development in the 21st Century - Between the Anthropocene and Anthropocentrism. IntechOpen. ISBN 978-1-83769-723-6 DOI: 10.5772/intechopen.1003274
16. Калабихина, И. Е., Казбекова, З. Г., & Мошкин, В. С. и др. (2024). Демографические аспекты отказа от курения (на основе данных социальных медиа и применения нейросетей). Искусственный интеллект в решении актуальных социальных и экономических проблем ХХI века: сборник статей по материалам Девятой всероссийской научно-практической конференции с международным участием (г. Пермь, 17-18октября 2024 г.). Пермь: Пермский национальный исследовательский политехнический университет, 359–364.
17. Мошкин, В. С., Кашин, М., Калабихина, И. Е., & Колотуша, А.В. (2023). Гибридный алгоритм классификации текстовых отзывов медицинской тематики из социальных медиаю X Международная конференция и молодёжная школа Информационные технологии и нанотехнологии (ИТНТ-2024). Самара: 231. https://scideck.ru/itnt2024/mysubs/231.
18. Калабихина, И. Е., Лукашевич, Н. В., Банин, Е. П., & Колотуша, А. В. (2023а). Эмоциональный фон публикаций электронных СМИ по теме материнского (семейного) капитала. Международная ежегодная научная конференция. Ломоносовские чтения - 2022. Секция экономических наук. Наука и искусство экономической политики в кризисных условиях Сборник лучших докладов / под ред. Г. И. Брялиной. Экономический факультет МГУ имени М. В. Ломоносова. М. 518–532.
Abstract (English)
Abstract
The database contains an upload of full text comments in Russian from the social media and online video sharing platform YouTube& It is collected in the period from March to August 2024. The database includes data on (non)smoking comments in .xls formats. In total, the database consists of 165 thousand publications, including sentiment classification. The subsamples of the database consist of 58 thousand publications and 5.5 thousand publications, all of them classified by (Generative) Artificial Intelligent. The first one includes information about the classified arguments on (non)smoking comments. The second one includes information about sex and age of commentator additionally, as well as arguments on (non)smoking comments in case when there exists the argument.
Keywords: database, (non)smoking arguments, social networks, YouTube, classification of age and sex, sensitive analysis, AI, GAI, Russia, LLM gemma2-9b-it
JEL codes: J1, Z18.
Other (English)
Acknowledgements
We are grateful to:
the Economic Faculty of Lomonosov Moscow State University for granting of our digital demography research in the framework of Scientific Project “Reproduction of population in socio-economic development”;
Public Joint-Stock Commercial Bank "DERZHAVA" for the co-funding of scientific journal Population and Economics that is the place for our data papers publications, and presentation of results.
Annotators – our students and PhD students.
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Additional details
Related works
- Has part
- Journal article: 10.22394/2304-3369-2024-5-48-67 (DOI)
Dates
- Collected
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2024-03/2024-08