Abstract
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 behavior was developed using thematic analysis of demographic content of search engines (concerning smoking cessation). According to our findings, health preservation is the predominant reason in arguments supporting 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 cessation (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 measures aimed at reducing smoking prevalence in Russia, potentially leading to quicker and more effective outcomes
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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.
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.
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.
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.
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.
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.
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.
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.
Sboev, A., Moloshnikov, I., Gudovski
kh, D., Selivanov, A., Rybka, R., & Litvinova, T.
(2018). Automatic gender identification of author
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