ARGUMENTS OF SOCIAL MEDIA USERS REGARDING SMOKING CESSATION (MACHINE LEARNING-BASED DATA)
EDN: NCZQMY
DOI:
https://doi.org/10.22394/1baejf91Abstract
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.
KEYWORDS: self-preserving behaviour, tobacco smoking, neural network methods, digital demography, machine learning, social networks, Russia
References
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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.
34. Safara, F., Mohammed, A. S., Potrus, M. Y., Ali, S., Tho, Q. T., Souri, A., Janenia F., Hosseinzadeh, M. (2020). An author gender detection method 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 Computer 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 analysis 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/10.1007/978-98119-9331-2_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. Procedia 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. Procedia computer science, 123, 417-423. https://doi.org/10.1016/j.procs.2018.01.064.
39. Sboev, A. G., Rybka, R. B., Moloshnikov, I. A., Naumov A. V., & Selivanov A. A. (2021). Comparison of the accuracies of methods based on language and graph neural network models for determining author profile features from russian texts. Vestnik natsional’nogo issledovatel’skogo 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. Development 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.
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