ДОВОДЫ ПОЛЬЗОВАТЕЛЕЙ СОЦИАЛЬНЫХ МЕДИА ПО ПОВОДУ ОТКАЗА ОТ ТАБАКОКУРЕНИЯ (НА ОСНОВЕ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ)
EDN: NCZQMY
DOI:
https://doi.org/10.22394/1baejf91Аннотация
АННОТАЦИЯ:
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.
КЛЮЧЕВЫЕ СЛОВА: самосохранительное поведение, табакокурение, нейросетевые методы, цифровая демография, машинное обучение, социальные сети, Россия
Библиографические ссылки
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21. Cabrera-Nguyen, E. P., Cavazos-Rehg, P., Krauss, M., Bierut, L. J., & Moreno, M. A. (2016). Young adults’ exposure to alcohol-and marijuana-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. International journal of environmental research and public health, 17(4), 1125. https://doi.org/10.3390/ ijerph17041125.
23. Curtis, B., Giorgi, S., Buffone, A. E., Ungar, L. H., Ashford, R. D., Hemmons, J., Summers, D., Hamilton, C., & Schwartz, H. A. (2018). Can Twitter be used to predict county excessive alcohol consumption rates? PloS One, 13(4), e0194290. https://doi.org/10.1371/journal.pone.0194290.
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31. Cheng, N., Chandramouli, R., & Subbalakshmi, K. P. (2011). Author gender identification from text. Digital Investigation, 8(1), 78-88. https://doi.org/10.1016/j.diin.2011.04.002.
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1. Liu, S., & Liu, J. (2021). Public attitudes toward COVID-19 vaccines on English-language Twitter: A sentiment analysis. Vaccine, 39(39), 5499-5505. https://doi.org/10.1016/j.vaccine.2021.08.058.
2. Huerta, D. T., Hawkins, J. B., Brownstein, J. S., & Hswen, Y. (2021). Exploring discussions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic mandate implementation: A Twitter analysis. SSM – Popul. Heal, 15. http://dx.doi.org/10.1016/j.ssmph.2021.100851.
3. Abosedra, S., Laopodis, N. T., & Fakih, A. (2021). Dynamics and asymmetries between consumer 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 influenza epidemics by analyzing Twitter messages. 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 influenza 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 Software, 45, 228-240. https://doi.org/10.1134/S0361768819050074.
7. Kotelnikov, E., Loukachevitch, N., Nikishina, I., & Panchenko, A. (2022). RuArg-2022: Argument Mining Evaluation. In Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2022”, 333-347. https://doi.org/10.48550/arXiv.2206.09249.
8. Prier, K. W., Smith, M. S., GiraudCarrier, 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/10.1007/9783-642-19656-0_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). Discovering health topics in social media using topic models. PloS One, 9(8), e103408. https://doi. org/10.1371/journal.pone.0103408.
11. Thackeray, R., Burton, S. H., Giraud-Carrier, 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 information 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 research, 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 medical 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., Nonnemaker, J., Lieberman, A. J., Hansen, H., & Porter, 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 conversations on Twitter. JMIR public health and surveillance, 2(2), 171. ttps://doi.org/10.2196/publichealth.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: uncovering 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 marijuana-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. International journal of environmental research and public health, 17(4), 1125. https://doi.org/10.3390/ijerph17041125.
23. Curtis, B., Giorgi, S., Buffone, A. E., Ungar, L. H., Ashford, R. D., Hemmons, J., Summers, D., Hamilton, C., & Schwartz, H. A. (2018). Can Twitter be used to predict county excessive alcohol 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 twitter—a topic model. American Journal of Health Education, 49(4), 256-263. https://doi.org/10.1080/19325037.2018.1473180.
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), 141147. https://doi.org/10.1177/14034948020300020 801.
26. Rosenthal, L., Carroll-Scott, A., Earnshaw, V. A., Sackey, N., O’Malley, S. S., Santilli, A., & Ickovics, J. R. (2013). Targeting cessation: understanding barriers and motivations to quitting among urban adult daily tobacco smokers. Addictive 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 systematic 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 programs. 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 quitting 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–Current 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., & Subbalakshmi, 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 identification 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 mathematics part 2, Studies in Computational Intelligence, vol 794, 39-54. https://doi.org/10.1007/9783-030-01632-6_3.
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