2304-3369 2308-8842 Вопросы управления Management issues 10.22394/2304-3369-2024-5-48-67 NCZQMY ДОВОДЫ ПОЛЬЗОВАТЕЛЕЙ СОЦИАЛЬНЫХ МЕДИА ПО ПОВОДУ ОТКАЗА ОТ ТАБАКОКУРЕНИЯ (НА ОСНОВЕ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ) ARGUMENTS OF SOCIAL MEDIA USERS REGARDING SMOKING CESSATION (MACHINE LEARNING-BASED DATA) Калабихина И. Е. Калабихина И. Е. Kalabikhina Irina E. ikalabikhina@yandex.ru 0000-0002-3958-6630 57190138890 N-3625-2013 Казбекова З. Г. Казбекова З. Г. Kazbekova Zarina G. kazbekova.zarina@bk.ru 0000-0002-7567-3184 57934120000 Зубова Е. А. Зубова Е. А. Zubova Ekaterina A. ez268@cornell.edu 0000-0003-3589-4772 Московский государственный университет им. М. В. Ломоносова (Москва, Россия) Lomonosov Moscow State University (Moscow, Russia) Корнеллский университет (Итака, Нью-Йорк, США) Cornell University (Ithaca, New York, USA) 09 03 2026 18 5 48 67 11 03 2024 20 07 2024 14 07 2024 CC BY-NC 4.0

Введение. Задачами настоящего исследования являются: 1) разработка алгоритма автоматизации доводов пользователей социальных медиа по вопросам в области самосохранительного поведения (мотивация курения либо отказа от курения); 2) структуризация причин (не)отказа от табакокурения русскоязычных пользователей на основе апробации разработанного алгоритма автоматизации доводов (не) бросать курить для аргументации мер демографической политики в перспективе. Материалы и методы. Алгоритм классификации доводов пользователей социальных медиа в пользу прекращения курения либо отказа от прекращения курения разработан с использованием методов обработки естественного языка на основе нейромодели Conversational RuBERT. Для обучения модели авторами собрано более 40 тысяч комментариев на русском языке, размещенных на платформе YouTube. Результаты. Сформирована система мнений русскоязычных пользователей YouTube по вопросам самосохранительного поведения на основе тематического анализа демографического контента поисковых систем (в отношении оставления привычки курить). По нашим данным, в аргументированных комментариях против курения преобладает мотив отказа по соображениям здоровьесбережения, по сравнению с аргументом о сбережении денежных средств. Также выявлено, что борьба с лишним весом служит причиной, по которой пользователи не желают бросать курить, но данный фактор не является ключевым. Точность предсказания классов в среднем превышает 85 %, что свидетельствует о достаточной надежности полученных результатов. Выводы. Разработанный авторами алгоритм автоматизации доводов (не) бросать курить позволит в режиме реального времени получать информацию о том, какой из факторов мешает россиянам бросить курить в большей степени (вред или дороговизна сигарет), насколько в российском обществе распространены те или иные мифы о вреде прекращения курения. Полученные данные могут использоваться для аргументации мер демографической политики в перспективе: в зависимости от полученных результатов меры политики по борьбе с курением могут быть настроены более оптимально, а значит, быстрее и эффективнее приведут к конечной цели – снижению распространенности курения в России.

Introduction. The objectives of this research are: 1) to develop an algorithm for automating the social media users’ arguments regarding self-preserving behavior (motivation to continue or quit smoking); and 2) to structure the reasons to continue or quit smoking among the Russian-speaking users, based on testing the developed automation algorithm to rest the future arguments of demographic policy measures. Materials and Methods. The algorithm to classify social media users’ arguments in favor of or against smoking cessation was developed using natural language processing methods based on the Conversational RuBERT neural model. The authors compiled a dataset of over 40,000 Russian-language comments posted on YouTube for model training. Results. A system of opinions among Russian-speaking YouTube users regarding self-preservation be havior was developed using thematic analysis of demographic content of search engines (concerning smok ing cessation). According to our findings, health preservation is the predominant reason in arguments sup porting smoking quitting, outweighing financial considerations. Additionally, the desire to avoid weight gain was identified as a reason some users choose not to quit smoking, although this factor is not a primary concern. On average, class prediction accuracy exceeds 85 %, indicating a high level of results reliability. Conclusions. The algorithm developed by the authors for automating the arguments related to smoking cessation provides real-time insights into the factors most strongly deterring Russians from smoking ces sation (whether health concerns or cost of cigarettes) and the prevalence of certain myths about smoking cessation in the Russian society. The obtained data can be used to better tailor demographic policy mea sures aimed at reducing smoking prevalence in Russia, potentially leading to quicker and more effective outcomes.

 Ключевые слова самосохранительное поведение табакокурение нейросетевые методы цифровая демография машинное обучение социальные сети Россия Keywords self-preserving behaviour tobacco smoking neural network methods digital demography machine learning social networks Russia Исследование выполнено в рамках НИР «Воспроизводство населения в социально-экономическом развитии» 122041800047-9 The study is part of the research project «Population reproduction in socio-economic development» 122041800047-9 1. Liu, S., & Liu, J. (2021). Public attitudes toward COVID-19 vaccines on English-lan guage Twitter: A sentiment analysis. Vaccine, 39(39), 5499-5505. https://doi.org/10.1016/j.vac cine.2021.08.058. 2. Huerta, D. T., Hawkins, J. B., Brownstein, J. S., & Hswen, Y. (2021). Exploring discus sions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic man date implementation: A Twitter analysis. SSM – Popul. Heal, 15. http://dx.doi.org/10.1016/j.ss mph.2021.100851. 3. Abosedra, S., Laopodis, N. T., & Fakih, A. (2021). Dynamics and asymmetries between con sumer sentiment and consumption in pre-and during-COVID-19 time: Evidence from the US. The Journal of Economic Asymmetries, 24, e00227. https://doi.org/10.1016/j.jeca.2021.e00227.social media analytics, 115-122. https://doi. org/10.1145/1964858.1964874. 5. Broniatowski, D. A., Paul, M. J., & Dredze, M. (2013). National and local influenza surveillance through Twitter: an analysis of the 2012-2013 in fluenza epidemic. PloS One, 8(12), e83672. https:// doi.org/10.1371/journal.pone.0083672. 6. Vychegzhanin, S. V., & Kotelnikov, E. V. (2019). Stance detection based on ensembles of classifiers. Programming and Computer Soft ware, 45, 228-240. S0361768819050074. https://doi.org/10.1134/ 7. Kotelnikov, E., Loukachevitch, N., Nikishi na, I., & Panchenko, A. (2022). RuArg-2022: Ar gument Mining Evaluation. In Computational Linguistics and Intellectual Technologies: Proceed ings of the International Conference “Dialogue 2022”, 4. Culotta, A. (2010). Towards detecting in fluenza epidemics by analyzing Twitter mes sages. In Proceedings of the first workshop on 333-347. https://doi.org/10.48550/arX iv.2206.09249. 8. Prier, K. W., Smith, M. S., Giraud Carrier, C., & Hanson, C. L. (2011). Identifying Health-Related Topics on Twitter. In Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2011, Lecture Notes in Computer Science, vol. 6589. https://doi.org/10.1007/978 3-642-19656-0_4. 9. Paul, M., & Dredze, M. (2011). You are what you tweet: Analyzing twitter for public health. In Proceedings of the International AAAI Conference on Web and Social Media, (1) 265-272). https:// doi.org/10.1609/icwsm.v5i1.14137. 10. Paul, M. J., & Dredze, M. (2014). Discov ering health topics in social media using top ic models. PloS One, 9(8), e103408. https://doi. org/10.1371/journal.pone.0103408. 11. Thackeray, R., Burton, S. H., Giraud-Car rier, C., Rollins, S., & Draper, C. R. (2013). Using Twitter for breast cancer prevention: an analysis of breast cancer awareness month. BMC cancer, 13, 1-9. https://doi.org/10.1186/1471-2407-13-508. 12. Kim, E., Hou, J., Han, J. Y., & Himelboim, I. (2016). Predicting retweeting behavior on breast cancer social networks: Network and content characteristics. Journal of health communication, 21(4), 479-486. https://doi.org/10.1080/10810730 .2015.1103326. 13. Himelboim, I., & Han, J. Y. (2014). Cancer talk on twitter: community structure and informa tion sources in breast and prostate cancer social networks. Journal of health communication, 19(2), 210-225. https://doi.org/10.1080/10810730.2013. 811321. 14. Sutton, J., Vos, S. C., Olson, M. K., Woods, C., Cohen, E., Gibson, C. B., & Butts, C. T. (2018). Lung cancer messages on Twitter: content analysis and evaluation. Journal of the American College of Radiology, 15(1), 210-217. https://doi. org/10.1016/j.jacr.2017.09.043. 15. Myslín, M., Zhu, S. H., Chapman, W., & Conway, M. (2013). Using twitter to examine smoking behavior and perceptions of emerging tobacco products. Journal of medical Internet re search, 15(8), e2534. https://doi.org/10.2196/ jmir.2534. 16. Cole-Lewis, H., Pugatch, J., Sanders, A., Varghese, A., Posada, S., Yun, C., Augustson, E. (2015a). Social listening: a content analysis of e-cigarette discussions on Twitter. Journal of med ical Internet research, 17(10), e243. https://doi. org/10.2196/jmir.4969. 17. Cole-Lewis, H., Varghese, A., Sanders, A., Schwarz, M., Pugatch, J., & Augustson, E. (2015). Assessing electronic cigarette-related tweets for sentiment and content using supervised machine learning. Journal of medical Internet research, 17(8), e208. https://doi.org/10.2196/jmir.4392. 18. Kim, A. E., Hopper, T., Simpson, S., Nonne maker, J., Lieberman, A. J., Hansen, H., & Por ter, L. (2015). Using Twitter data to gain insights into e-cigarette marketing and locations of use: an infoveillance study. Journal of medical Internet research, 17(11), e251. https://doi.org/10.2196/ jmir.4466. 19. Lazard, A. J., Saffer, A. J., Wilcox, G. B., Chung, A. D., Mackert, M. S., & Bernhardt, J. M. (2016). E-cigarette social media messages: a text mining analysis of marketing and consumer con versations on Twitter. JMIR public health and sur veillance, 2(2), 171. https://doi.org/10.2196/publi chealth.6551. 20. Clark, E. M., Jones, C. A., Williams, J. R., Kurti, A. N., Norotsky, M. C., Danforth, C. M., & Dodds, P. S. (2016). Vaporous marketing: uncov ering pervasive electronic cigarette advertisements on Twitter. PLoS One, 11(7), e0157304. https:// doi.org/10.1371/journal.pone.0157304. 21. Cabrera-Nguyen, E. P., Cavazos-Rehg, P., Krauss, M., Bierut, L. J., & Moreno, M. A. (2016). Young adults’ exposure to alcohol-and marijua na-related content on Twitter. Journal of studies on alcohol and drugs, 77(2), 349-353. https://doi. org/10.15288/jsad.2016.77.349. 22. Giorgi, S., Yaden, D. B., Eichstaedt, J. C., Ashford, R. D., Buffone, A. E., Schwartz, H. A., Ungar, L.H., Curtis, B. (2020). Cultural differences in Tweeting about drinking across the US. Interna tional journal of environmental research and pub lic health, 17(4), 1125. https://doi.org/10.3390/ ijerph17041125. 23. Curtis, B., Giorgi, S., Buffone, A. E., Un gar, L. H., Ashford, R. D., Hemmons, J., Summers, D., Hamilton, C., & Schwartz, H. A. (2018). Can Twitter be used to predict county excessive alco hol consumption rates? PloS One, 13(4), e0194290. https://doi.org/10.1371/journal.pone.0194290. 24. Barry, A. E., Valdez, D., Padon, A. A., & Russell, A. M. (2018). Alcohol advertising on twit ter—a topic model. American Journal of Health Education, 49(4), 256-263. https://doi.org/10.108 0/19325037.2018.1473180. 25. Helgason, A. R., & Lund, K. E. (2002). General practitioners’ perceived barriers to smoking cessation-results from four Nordic countries. Scandinavian journal of public health, 30(2), 141-147. https://doi.org/10.1177/14034948020300020801. 26. Rosenthal, L., Carroll-Scott, A., Earnshaw, V. A., Sackey, N., O’Malley, S. S., Santilli, A., & Ickovics, J. R. (2013). Targeting cessation: un derstanding barriers and motivations to quitting among urban adult daily tobacco smokers. Ad dictive Behaviors, 38(3), 1639-1642. https://doi. org/10.1016/j.addbeh.2012.09.016. 27. Twyman, L., Bonevski, B., Paul, C., & Bryant, J. (2014). Perceived barriers to smoking cessation in selected vulnerable groups: a sys tematic review of the qualitative and quantitative literature. BMJ open, 4(12), e006414. https://doi. org/10.1136/bmjopen-2014-006414. 28. Pagano, A., Tajima, B., & Guydish, J. (2016). Barriers and facilitators to tobacco cessation in a nationwide sample of addiction treatment pro grams. Journal of substance abuse treatment, 67, 22-29. https://doi.org/10.1016/j.jsat.2016.04.004. 29. Carlson, S., Widome, R., Fabian, L., Luo, X., & Forster, J. (2018). Barriers to quit ting smoking among young adults: the role of socioeconomic status. American Journal of Health Promotion, 32(2), 294-300. https://doi. org/10.1177/0890117117696350. 30. Gupta, R., Pednekar, M. S., Kumar, R., & Goel, S. (2021). Tobacco cessation in India–Cur rent status, challenges, barriers and solutions. Indian Journal of Tuberculosis, 68, S80-S85. https:// doi.org/10.1016/j.ijtb.2021.08.027. 31. Cheng, N., Chandramouli, R., & Subbal akshmi, K. P. (2011). Author gender identification from text. Digital Investigation, 8(1), 78-88. https://doi.org/10.1016/j.diin.2011.04.002. 32. Alsmearat, K., Al-Ayyoub, M., Al-Shalabi, R., & Kanaan, G. (2017). Author gender iden tification from Arabic text. Journal of Information Security and Applications, 35, 85-95. https://doi. org/10.1016/j.jisa.2017.06.003. 33. Vicente, M., Batista, F., & Carvalho, J. P. (2019). Gender detection of Twitter users based on multiple information sources. In Interactions between computational intelligence and math ematics part 2, Studies in Computational Intelligence, vol 794, 39-54. https://doi.org/10.1007/978 3-030-01632-6_3. 34. Safara, F., Mohammed, A. S., Potrus, M. Y., Ali, S., Tho, Q. T., Souri, A., Janenia F., Hosseinza deh, M. (2020). An author gender detection meth od using whale optimization algorithm and artificial neural network. IEEE Access, 8, 48428-48437. https://doi.org/10.1109/ACCESS.2020.2973509. 35. Ouni, S., Fkih, F., Omri, M. N. (2022). Bots and Gender Detection on Twitter Using Stylistic Features. In Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Com puter and Information Science, vol 1653. https:// doi.org/10.1007/978-3-031-16210-7_53. 36. Zainab, Z., Al-Obeidat, F., Moreira, F., Gul, H., & Amin, A. (2023). Comparative analy sis of machine learning algorithms for author age and gender identification. In Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 614. https://doi.org/10.1007/978-981 19-9331-2_11. 37. Sboev, A., Litvinova, T., Gudovskikh, D., Rybka, R., & Moloshnikov, I. (2016). Machine learning models of text categorization by author gender using topic-independent features. Proce dia Computer Science, 101, 135-142. https://doi. org/10.1016/j.procs.2016.11.017. 38. Sboev, A., Moloshnikov, I., Gudovskikh, D., Selivanov, A., Rybka, R., & Litvinova, T. (2018). Automatic gender identification of author of Russian text by machine learning and neural net algorithms in case of gender deception. Pro cedia computer science, 123, 417-423. https://doi. org/10.1016/j.procs.2018.01.064. 39. Сбоев А. Г., Рыбка Р. Б., Молошников И. А., Наумов А. В., Селиванов А. А. Сравнение точностей методов на основе языковых и графовых нейросетевых моделей для определения признаков авторского профиля по текстам на русском языке // Вестник НИЯУ МИФИ. 2023. Т. 10. № 6. С. 529-539. DOI: 10.56304/S2304487X21060109. EDN: WHAVGC. 40. Scholten, H., Luijten, M., & Granic, I. (2019). A randomized controlled trial to test the effectiveness of a peer-based social mobile game intervention to reduce smoking in youth. Devel opment and Psychopathology, 31(5), 1923-1943. https://doi.org/10.1017/S0954579419001378. 41. 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. https://doi.org/10.3897/ popecon.7.e97064. 42. Калабихина И. Е., Казбекова З. Г., Банин Е. П., Клименко Г. А. Демографические ценности и социально-демографический портрет пользователей ВКонтакте: есть ли связь? // Вестник Московского университета. Серия 6. Экономика. 2023. №. 3. С. 157-180. DOI: 10.55959/MSU0130-0105-6-58-3-8. EDN: EXRUCA. 43. Кузнецова П. О. Почему не снижается курение у женщин: результаты микроанали за // Женщина в российском обществе. 2019. № 3. С. 91–101. DOI: 10.21064/WinRS.2019.3.7. 1. Liu, S., & Liu, J. (2021). Public attitudes toward COVID-19 vaccines on English-lan guage Twitter: A sentiment analysis. Vaccine, 39(39), 5499-5505. https://doi.org/10.1016/j.vac cine.2021.08.058. 2. Huerta, D. T., Hawkins, J. B., Brown stein, J. S., & Hswen, Y. (2021). Exploring discus sions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic man date implementation: A Twitter analysis. SSM – Popul. Heal, 15. http://dx.doi.org/10.1016/j.ss mph.2021.100851. 3. Abosedra, S., Laopodis, N. T., & Fakih, A. (2021). Dynamics and asymmetries between con sumer sentiment and consumption in pre-and during-COVID-19 time: Evidence from the US. The Journal of Economic Asymmetries, 24, e00227. https://doi.org/10.1016/j.jeca.2021.e00227. 4. Culotta, A. (2010). Towards detecting in f luenza epidemics by analyzing Twitter mes sages. In Proceedings of the first workshop on social media analytics, 115-122. https://doi. org/10.1145/1964858.1964874. 5. Broniatowski, D. A., Paul, M. J., & Dredze, M. (2013). National and local influenza surveillance through Twitter: an analysis of the 2012-2013 in f luenza epidemic. PloS One, 8(12), e83672. https:// doi.org/10.1371/journal.pone.0083672. 6. Vychegzhanin, S. V., & Kotelnikov, E. V. (2019). Stance detection based on ensembles of classifiers. Programming and Computer Soft ware, 45, 228-240. S0361768819050074. https://doi.org/10.1134/ 7. Kotelnikov, E., Loukachevitch, N., Nikishi na, I., & Panchenko, A. (2022). RuArg-2022: Ar gument Mining Evaluation. In Computational Linguistics and Intellectual Technologies: Proceed ings of the International Conference “Dialogue 2022”, 333-347. https://doi.org/10.48550/arX iv.2206.09249. 8. Prier, K. W., Smith, M. S., Giraud Carrier, C., & Hanson, C. L. (2011). Identifying Health-Related Topics on Twitter. In Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2011, Lecture Notes in Computer Science, vol. 6589. https://doi.org/10.1007/978 3-642-19656-0_4. 9. Paul, M., & Dredze, M. (2011). You are what you tweet: Analyzing twitter for public health. In Proceedings of the International AAAI Conference on Web and Social Media, (1) 265-272). https:// doi.org/10.1609/icwsm.v5i1.14137. 10. Paul, M. J., & Dredze, M. (2014). Discov ering health topics in social media using top ic models. PloS One, 9(8), e103408. https://doi. org/10.1371/journal.pone.0103408. 11. Thackeray, R., Burton, S. H., Giraud-Car rier, C., Rollins, S., & Draper, C. R. (2013). Using Twitter for breast cancer prevention: an analysis of breast cancer awareness month. BMC cancer, 13, 1-9. https://doi.org/10.1186/1471-2407-13-508. 12. Kim, E., Hou, J., Han, J. Y., & Himelboim, I. (2016). Predicting retweeting behavior on breast cancer social networks: Network and content characteristics. Journal of health communication, 21(4), 479-486. https://doi.org/10.1080/10810730 .2015.1103326. 13. Himelboim, I., & Han, J. Y. (2014). Cancer talk on twitter: community structure and informa tion sources in breast and prostate cancer social networks. Journal of health communication, 19(2), 210-225. https://doi.org/10.1080/10810730.2013. 811321. 14. Sutton, J., Vos, S. C., Olson, M. K., Woods, C., Cohen, E., Gibson, C. B., & Butts, C. T. (2018). Lung cancer messages on Twitter: content analysis and evaluation. Journal of the American College of Radiology, 15(1), 210-217. https://doi. org/10.1016/j.jacr.2017.09.043. 15. Myslín, M., Zhu, S. H., Chapman, W., & Conway, M. (2013). Using twitter to examine smoking behavior and perceptions of emerging tobacco products. Journal of medical Internet re search, 15(8), e2534. https://doi.org/10.2196/ jmir.2534. 16. Cole-Lewis, H., Pugatch, J., Sanders, A., Varghese, A., Posada, S., Yun, C., Augustson, E. (2015a). Social listening: a content analysis of e-cigarette discussions on Twitter. Journal of med ical Internet research, 17(10), e243. https://doi. org/10.2196/jmir.4969. 17. Cole-Lewis, H., Varghese, A., Sanders, A., Schwarz, M., Pugatch, J., & Augustson, E. (2015). Assessing electronic cigarette-related tweets for sentiment and content using supervised machine learning. Journal of medical Internet research, 17(8), e208. https://doi.org/10.2196/jmir.4392. 18. Kim, A. E., Hopper, T., Simpson, S., Nonne maker, J., Lieberman, A. J., Hansen, H., & Por ter, L. (2015). Using Twitter data to gain insights into e-cigarette marketing and locations of use: an infoveillance study. Journal of medical Internet research, 17(11), e251. https://doi.org/10.2196/ jmir.4466. 19. Lazard, A. J., Saffer, A. J., Wilcox, G. B., Chung, A. D., Mackert, M. S., & Bernhardt, J. M. (2016). E-cigarette social media messages: a text mining analysis of marketing and consumer con versations on Twitter. JMIR public health and sur veillance, 2(2), 171. https://doi.org/10.2196/publi chealth.6551. 20. Clark, E. M., Jones, C. A., Williams, J. R., Kurti, A. N., Norotsky, M. C., Danforth, C. M., & Dodds, P. S. (2016). Vaporous marketing: uncov ering pervasive electronic cigarette advertisements on Twitter. PLoS One, 11(7), e0157304. https:// doi.org/10.1371/journal.pone.0157304. 21. Cabrera-Nguyen, E. P., Cavazos-Rehg, P., Krauss, M., Bierut, L. J., & Moreno, M. A. (2016). Young adults’ exposure to alcohol-and marijua na-related content on Twitter. Journal of studies on alcohol and drugs, 77(2), 349-353. https://doi. org/10.15288/jsad.2016.77.349. 22. Giorgi, S., Yaden, D. B., Eichstaedt, J. C., Ashford, R. D., Buffone, A. E., Schwartz, H. A., Ungar, L.H., Curtis, B. (2020). Cultural differences in Tweeting about drinking across the US. Interna tional journal of environmental research and pub lic health, 17(4), 1125. https://doi.org/10.3390/ ijerph17041125. 23. Curtis, B., Giorgi, S., Buffone, A. E., Un gar, L. H., Ashford, R. D., Hemmons, J., Summers, D., Hamilton, C., & Schwartz, H. A. (2018). Can Twitter be used to predict county excessive alco hol consumption rates? PloS One, 13(4), e0194290. https://doi.org/10.1371/journal.pone.0194290. 24. Barry, A. E., Valdez, D., Padon, A. A., & Russell, A. M. (2018). Alcohol advertising on twit ter—a topic model. American Journal of Health Education, 49(4), 256-263. https://doi.org/10.108 0/19325037.2018.1473180. 25. Helgason, A. R., & Lund, K. E. (2002). General practitioners’ perceived barriers to smok ing cessation-results from four Nordic countries. Scandinavian journal of public health, 30(2), 141 147. https://doi.org/10.1177/14034948020300020 801. 26. Rosenthal, L., Carroll-Scott, A., Earn shaw, V. A., Sackey, N., O’Malley, S. S., Santilli, A., & Ickovics, J. R. (2013). Targeting cessation: un derstanding barriers and motivations to quitting among urban adult daily tobacco smokers. Ad dictive Behaviors, 38(3), 1639-1642. https://doi. org/10.1016/j.addbeh.2012.09.016. 27. Twyman, L., Bonevski, B., Paul, C., & Bryant, J. (2014). Perceived barriers to smoking cessation in selected vulnerable groups: a sys tematic review of the qualitative and quantitative literature. BMJ open, 4(12), e006414. https://doi. org/10.1136/bmjopen-2014-006414. 28. Pagano, A., Tajima, B., & Guydish, J. (2016). Barriers and facilitators to tobacco cessation in a nationwide sample of addiction treatment pro grams. Journal of substance abuse treatment, 67, 22-29. https://doi.org/10.1016/j.jsat.2016.04.004. 29. Carlson, S., Widome, R., Fabian, L., Luo, X., & Forster, J. (2018). Barriers to quit ting smoking among young adults: the role of socioeconomic status. American Journal of Health Promotion, 32(2), 294-300. https://doi. org/10.1177/0890117117696350. 30. Gupta, R., Pednekar, M. S., Kumar, R., & Goel, S. (2021). Tobacco cessation in India–Cur rent status, challenges, barriers and solutions. In dian Journal of Tuberculosis, 68, S80-S85. https:// doi.org/10.1016/j.ijtb.2021.08.027. 31. Cheng, N., Chandramouli, R., & Subbal akshmi, K. P. (2011). Author gender identifica tion from text. Digital Investigation, 8(1), 78-88. https://doi.org/10.1016/j.diin.2011.04.002. 32. Alsmearat, K., Al-Ayyoub, M., Al-Shala bi, R., & Kanaan, G. (2017). Author gender iden tification from Arabic text. Journal of Information Security and Applications, 35, 85-95. https://doi. org/10.1016/j.jisa.2017.06.003. 33. Vicente, M., Batista, F., & Carvalho, J. P. (2019). Gender detection of Twitter users based on multiple information sources. In Interactions between computational intelligence and math ematics part 2, Studies in Computational Intelli gence, vol 794, 39-54. https://doi.org/10.1007/978 3-030-01632-6_3. 34. Safara, F., Mohammed, A. S., Potrus, M. Y., Ali, S., Tho, Q. T., Souri, A., Janenia F., Hosseinza deh, M. (2020). An author gender detection meth od using whale optimization algorithm and artifi cial neural network. IEEE Access, 8, 48428-48437. https://doi.org/10.1109/ACCESS.2020.2973509. 35. Ouni, S., Fkih, F., Omri, M. N. (2022). Bots and Gender Detection on Twitter Using Stylistic Features. In Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Com puter and Information Science, vol 1653. https:// doi.org/10.1007/978-3-031-16210-7_53. 36. Zainab, Z., Al-Obeidat, F., Moreira, F., Gul, H., & Amin, A. (2023). Comparative analy sis of machine learning algorithms for author age and gender identification. In Proceedings of Inter national Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 614. https://doi.org/10.1007/978-981 19-9331-2_11. 37. Sboev, A., Litvinova, T., Gudovskikh, D., Rybka, R., & Moloshnikov, I. (2016). Machine learning models of text categorization by author gender using topic-independent features. Proce dia Computer Science, 101, 135-142. https://doi. org/10.1016/j.procs.2016.11.017. 38. Sboev, A., Moloshnikov, I., Gudovski kh, D., Selivanov, A., Rybka, R., & Litvinova, T. (2018). Automatic gender identification of author of Russian text by machine learning and neural net algorithms in case of gender deception. Pro cedia computer science, 123, 417-423. https://doi. org/10.1016/j.procs.2018.01.064. 39. Sboev, A. G., Rybka, R. B., Molosh nikov, I. A., Naumov A. V., & Selivanov A. A. (2021). Comparison of the accuracies of methods based on language and graph neural network mod els for determining author profile features from russian texts. Vestnik natsional’nogo issledovatel’sk ogo yadernogo universiteta “MIFI”, 10(6), 529-539. https://doi.org/10.56304/S2304487X21060109. https://elibrary.ru/WHAVGC. 40. Scholten, H., Luijten, M., & Granic, I. (2019). A randomized controlled trial to test the effectiveness of a peer-based social mobile game intervention to reduce smoking in youth. Devel opment and Psychopathology, 31(5), 1923-1943. https://doi.org/10.1017/S0954579419001378. 41. Kalabikhina, I., Zubova, E., Louk achevitch, N., Kolotusha, A., Kazbekova, Z., Banin, E., & Klimenko, G. (2023). Identifying Reproductive Behavior Arguments in Social Me dia Content Users’ Opinions through Natural Language Processing Techniques. Population and Economics, 7(2), 40-59. https://doi.org/10.3897/ popecon.7.e97064. 42. Kalabikhina, I. E., Kazbekova, Z. G., Banin, E. P., & Klimenko, G. A. (2023). Demographic valuesand socio-demographic pro f ile of Vkontakte users: is there a connection? Moscow University Economics Bulletin, 3, 157-180. https://doi.org/10.55959/MSU0130-0105-6-58-3 8. https://elibrary.ru/EXRUCA. 43. Kuznetsova, P. O. (2019). Why the num ber of smoking women does not decrease: a view from the microanalysis level. Woman in russian society, 3, 91-101. https://doi.org/10.21064/ WinRS.2019.3.7.