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
Библиографические ссылки
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.
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.
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.
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.
Vychegzhanin, S. V., & Kotelnikov, E. V.
(2019). Stance detection based on ensembles
of classifiers. Programming and Computer Soft
ware, 45, 228-240.
S0361768819050074.
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”,
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.
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.
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.
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.
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.
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.
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.
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
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org/10.1016/j.jacr.2017.09.043.
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.
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.
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.
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.
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.
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.
Cabrera-Nguyen, E. P., Cavazos-Rehg, P.,
Krauss, M., Bierut, L. J., & Moreno, M. A. (2016).
Young adults’ exposure to alcohol-and marijua
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on alcohol and drugs, 77(2), 349-353. https://doi.
org/10.15288/jsad.2016.77.349.
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.
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.
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.
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.
Rosenthal, L., Carroll-Scott, A., Earnshaw, V. A., Sackey, N., O’Malley, S. S., Santilli, A., & Ickovics, J. R. (2013). Targeting cessation: un
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org/10.1016/j.addbeh.2012.09.016.
Twyman, L., Bonevski, B., Paul, C., & Bryant, J. (2014). Perceived barriers to smoking
cessation in selected vulnerable groups: a sys
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literature. BMJ open, 4(12), e006414. https://doi.
org/10.1136/bmjopen-2014-006414.
Pagano, A., Tajima, B., & Guydish, J. (2016).
Barriers and facilitators to tobacco cessation in a
nationwide sample of addiction treatment pro
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Carlson, S., Widome, R., Fabian, L., Luo, X., & Forster, J. (2018). Barriers to quit
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Health Promotion, 32(2), 294-300. https://doi.
org/10.1177/0890117117696350.
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cine.2021.08.058.
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.
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.
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.
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.
Vychegzhanin, S. V., & Kotelnikov, E. V.
(2019). Stance detection based on ensembles
of classifiers. Programming and Computer Soft
ware,
45,
228-240.
S0361768819050074.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
https://doi.org/10.1177/14034948020300020
801.
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.
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.
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.
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.
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.
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