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Assessing the Surge in COVID-19-Related Cyberbullying on Twitter: A Generalized Additive Model Approach

Year 2023, Volume: 20 Issue: Human Behavior and Social Institutions, 1014 - 1028, 30.10.2023

Yavuz Selim Balcıoğlu , Kültigin Akçin

https://doi.org/10.26466/opusjsr.1349492

Abstract

The COVID-19 pandemic's onset and the subsequent lockdowns drastically amplified digital interactions worldwide. These unparalleled shifts in online behavior birthed concerns about potential surges in cybersecurity threats, particularly cyberbullying. Our research aimed to explore these proposed trends on Twitter. Utilizing a dataset of 126,348 tweets from January 1st to September 12th, 2020, we honed in on 27 cyberbullying-related keywords, like 'online bullying' and 'cyberbullying'. Recognizing the limitations of traditional change-point models, we opted for a Generalized Additive Model (GAM) with spline-based smoothers. The results were revealing. A significant uptick in cyberbullying instances emerged starting mid-March, correlating with the global lockdown mandates. This consistent trend was evident across all our targeted keywords. To bolster our findings, we conducted lag-based assessments and compared the GAM against other modeling approaches. Our conclusions robustly indicate a strong association between the enforcement of pandemic lockdowns and a heightened prevalence of cyberbullying on Twitter. The implications are clear: global crises necessitate intensified cyber vigilance, and the digital realm's safety becomes even more paramount during such challenging times.

Keywords

covid19 Cyberbullying Twitter GAM

References

  • Achuthan, K., Nair, V. K., Kowalski, R., Ramanathan, S., & Raman, R. (2023). Cyberbullying research — Alignment to sustainable development and impact of COVID-19: Bibliometrics and science mapping analysis. Computers in Human Behavior, 140, 107566. https://doi.org/10.1016/j.chb.2022.107566
  • Balakrishnan, V., Khan, S., & Arabnia, H. R. (2020). Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Computers & Security, 90, 101710. https://doi.org/10.1016/j.cose.2019.101710
  • Brandt, P. T., & Sandler, T. (2012). A Bayesian Poisson vector autoregression model. Political Analysis, 20(3), 292-315.
  • Bonanno, R. A., & Hymel, S. (2013). Cyber Bullying and Internalizing Difficulties: Above and Beyond the Impact of Traditional Forms of Bullying. Journal of Youth and Adolescence, 42(5), 685-697. https://doi.org/10.1007/s10964-013-9937-1
  • Cerna, M. A. (2015). The Chinese “Togetherness-Separation” Paradox: An Analytical Approach to Understanding Chinese People’s Behavior and Its Implication to International Cooperation. International Journal of Business and Management, 10(12), 194. https://doi.org/10.5539/ijbm.v10n12p194
  • Chelmis, C., Zois, D.-S., & Yao, M. (2017). Mining Patterns of Cyberbullying on Twitter. 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 126-133. https://doi.org/10.1109/ICDMW.2017.22
  • Cheng, L., Shu, K., Wu, S., Silva, Y. N., Hall, D. L., & Liu, H. (2020). Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model (arXiv:2008.02642). arXiv. https://doi.org/10.48550/arXiv.2008.02642
  • Cuadrado-Gordillo, I., & Fernández-Antelo, I. (2016). Vulnerability and Mimicry as Predictive Axes in Cyberbullying. Journal of Interpersonal Violence, 31(1), 81-99. https://doi.org/10.1177/0886260514555128
  • Das, S., Kim, A., & Karmakar, S. (2020). Change-point analysis of cyberbullying-related twitter discussions during covid-19. arXiv preprint arXiv:2008.13613.
  • Dewani, A., Memon, M. A., & Bhatti, S. (2021). Development of computational linguistic resources for automated detection of textual cyberbullying threats in Roman Urdu language. 3 c TIC: Cuadernos de Desarrollo Aplicados a Las TIC, 10(2), 101-121.
  • Dredge, R., Gleeson, J. F. M., & de la Piedad Garcia, X. (2014). Risk Factors Associated with Impact Severity of Cyberbullying Victimization: A Qualitative Study of Adolescent Online Social Networking. Cyberpsychology, Behavior, and Social Networking, 17(5), 287-291. https://doi.org/10.1089/cyber.2013.0541
  • Dominici, F., McDermott, A., Zeger, S. L., & Samet, J. M. (2002). On the use of generalized additive models in time-series studies of air pollution and health. American journal of epidemiology, 156(3), 193-203.
  • Everbach, T., Clark, M., & Nisbett, G. S. (2018). #IfTheyGunnedMeDown: An Analysis of Mainstream and Social Media in the Ferguson, Missouri, Shooting of Michael Brown. Electronic News, 12(1), 23-41. https://doi.org/10.1177/1931243117697767
  • Feldman, L., Hart, P. S., & Milosevic, T. (2017). Polarizing news? Representations of threat and efficacy in leading US newspapers’ coverage of climate change. Public Understanding of Science, 26(4), 481-497. https://doi.org/10.1177/0963662515595348
  • Giménez Gualdo, A. M., Hunter, S. C., Durkin, K., Arnaiz, P., & Maquilón, J. J. (2015). The emotional impact of cyberbullying: Differences in perceptions and experiences as a function of role. Computers & Education, 82, 228-235. https://doi.org/10.1016/j.compedu.2014.11.013
  • Hosseinmardi, H., Mattson, S. A., Ibn Rafiq, R., Han, R., Lv, Q., & Mishra, S. (2015). Analyzing Labeled Cyberbullying Incidents on the Instagram Social Network. Içinde T.-Y. Liu, C. N. Scollon, & W. Zhu (Ed.), Social Informatics (ss. 49-66). Springer International Publishing. https://doi.org/10.1007/978-3-319-27433-1_4
  • Huang, Q., Singh, V. K., & Atrey, P. K. (2018). On cyberbullying incidents and underlying online social relationships. Journal of Computational Social Science, 1, 241-260.
  • Islam, M. M., Uddin, M. A., Islam, L., Akter, A., Sharmin, S., & Acharjee, U. K. (2020). Cyberbullying Detection on Social Networks Using Machine Learning Approaches. 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 1-6. https://doi.org/10.1109/CSDE50874.2020.9411601
  • Johanis, M. A., Bakar, A. R. A., & Ismail, F. (2020, April). Cyber-Bullying Trends Using Social Media Platform: An Analysis through Malaysian Perspectives. In Journal of Physics: Conference Series (Vol. 1529, No. 2, p. 022077). IOP Publishing.
  • Kee, D. M. H., Al-Anesi, M. A. L., & Al-Anesi, S. A. L. (2022). Cyberbullying on social media under the influence of COVID-19. Global Business and Organizational Excellence, 41(6), 11-22. https://doi.org/10.1002/joe.22175
  • López-Meneses, E., Vázquez-Cano, E., González-Zamar, M. D., & Abad-Segura, E. (2020). Socioeconomic effects in cyberbullying: Global research trends in the educational context. International journal of environmental research and public health, 17(12), 4369.
  • McHugh, M. (1997). The stress factor: Another item for the change management agenda? Journal of Organizational Change Management, 10(4), 345-362. https://doi.org/10.1108/09534819710175866
  • McHugh, M. C., Saperstein, S. L., & Gold, R. S. (2019). OMG U #Cyberbully! An Exploration of Public Discourse About Cyberbullying on Twitter. Health Education & Behavior, 46(1), 97-105. https://doi.org/10.1177/1090198118788610
  • McLoughlin, L. T., Lagopoulos, J., & Hermens, D. F. (2020). Cyberbullying and Adolescent Neurobiology. Frontiers in Psychology, 11. https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01511
  • Munira, S., Sener, I. N., & Dai, B. (2020). A Bayesian spatial Poisson-lognormal model to examine pedestrian crash severity at signalized intersections. Accident Analysis & Prevention, 144, 105679.
  • Ogolla, E., Kwanya, T., Kibe, L., Kogos, A., & Onsare, C. (2023). Curbing cyberbullying on Facebook: An analysis of mitigation strategies in universities in Kenya. Information Impact: Journal of Information and Knowledge Management, 14(1), Article 1. https://doi.org/10.4314/iijikm.v14i1.1
  • Ravindra, K., Rattan, P., Mor, S., & Aggarwal, A. N. (2019). Generalized additive models: Building evidence of air pollution, climate change and human health. Environment international, 132, 104987.
  • Saravanaraj, A., Sheeba, J. I., & Devaneyan, S. P. (2016). Automatic detection of cyberbullying from twitter. International Journal of Computer Science and Information Technology & Security (IJCSITS).
  • Sterner, G., & Felmlee, D. (2017). The Social Networks of Cyberbullying on Twitter. International Journal of Technoethics (IJT), 8(2), 1-15. https://doi.org/10.4018/IJT.2017070101
  • Taddy, M. A. (2010). Autoregressive mixture models for dynamic spatial Poisson processes: Application to tracking intensity of violent crime. Journal of the American Statistical Association, 105(492), 1403-1417.
  • Wainwright, P. E., Leatherdale, S. T., & Dubin, J. A. (2007). Advantages of mixed effects models over traditional ANOVA models in developmental studies: a worked example in a mouse model of fetal alcohol syndrome. Developmental Psychobiology: The Journal of the International Society for Developmental Psychobiology, 49(7), 664-674.
  • Wang, J., Fu, K., & Lu, C. T. (2020, December). Sosnet: A graph convolutional network approach to fine-grained cyberbullying detection. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 1699-1708). IEEE.
  • Weinberg, J., Brown, L. D., & Stroud, J. R. (2007). Bayesian forecasting of an inhomogeneous Poisson process with applications to call center data. Journal of the American Statistical Association, 102(480), 1185-1198.
  • Wisniewski, P., Jia, H., Wang, N., Zheng, S., Xu, H., Rosson, M. B., & Carroll, J. M. (2015). Resilience Mitigates the Negative Effects of Adolescent Internet Addiction and Online Risk Exposure. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 4029-4038. https://doi.org/10.1145/2702123.2702240
  • Wright, B. (2019). Weird Internet Aesthetics: Are Lo-Fi Media Inherently Revolutionary? English Honors Theses. https://creativematter.skidmore.edu/eng_stu_schol/26
  • Xu, Y., & Trzaskawka, P. (2021). Towards Descriptive Adequacy of Cyberbullying: Interdisciplinary Studies on Features, Cases and Legislative Concerns of Cyberbullying. International Journal for the Semiotics of Law - Revue Internationale de Sémiotique Juridique, 34(4), 929-943. https://doi.org/10.1007/s11196-021-09856-4
  • Verdier, G., Hilgert, N., & Vila, J. P. (2008). Optimality of cusum rule approximations in change-point detection problems: application to nonlinear state–space systems. IEEE Transactions on Information Theory, 54(11), 5102-5112.
  • Yang, F. (2021). Coping strategies, cyberbullying behaviors, and depression among Chinese netizens during the COVID-19 pandemic: A web-based nationwide survey. Journal of Affective Disorders, 281, 138-144. https://doi.org/10.1016/j.jad.2020.12.023
  • Zamba, K. D., & Hawkins, D. M. (2006). A multivariate change-point model for statistical process control. Technometrics, 48(4), 539-549.

Year 2023, Volume: 20 Issue: Human Behavior and Social Institutions, 1014 - 1028, 30.10.2023

Yavuz Selim Balcıoğlu , Kültigin Akçin

https://doi.org/10.26466/opusjsr.1349492

Abstract

COVID-19 salgınının küresel başlangıcı, evde kalma talimatlarına yol açarak dijital etkileşimlerde artışa yol açmıştır. Bu artan dijital katılımın, başta siber zorbalık olmak üzere siber güvenlik tehditlerini artırdığı varsayılmaktadır. Bu araştırma, COVID-19 salgınının Twitter'daki siber zorbalık eğilimleri üzerindeki etkisini niceliksel olarak ölçmeyi amaçlamaktadır. Veri çıkarmak için Python kitaplıklarını kullanarak 1 Ocak 2020'den 12 Eylül 2020'ye kadar uzanan, herkese açık 126.348 tweet'ten oluşan bir veri kümesi topladık. 'Çevrimiçi zorbalık', 'siber zorbalık' ve 'Twitter zorbalığı' gibi siber zorbalığa bağlı 18 spesifik anahtar kelimeye odaklanarak, ilgili siber zorbalık örneklerini belirlemeye çalıştık. Analitik çalışmalarımızda, karmaşık dalgalanmaları yakalamada yetersiz kalabilecek geleneksel bir değişim noktası modelini benimsemek yerine, spline tabanlı yumuşatıcılara sahip bir Genelleştirilmiş Toplama Modeli (GAM) kullandık. Bu yaklaşım, Mart ortasından itibaren siber zorbalık faaliyetlerinde belirgin bir artışı ustaca ortaya çıkarmıştır ve evde kalma protokollerinin küresel uygulamasıyla uyumlu hale geldiği tespi edilmiştir. Gözlemlenen bu eğilim, odak noktasının toplu anahtar kelime sayısı mı yoksa tek tek anahtar kelime örnekleri mi olduğuna bakılmaksızın doğrulanmıştır. Analizimizi daha da zenginleştirerek gecikmeye dayalı değerlendirmeleri dikkate aldık ve seçtiğimiz GAM metodolojisini alternatif modelleme stratejileriyle karşılaştırdık. Toplu olarak, içgörülerimiz, pandemiyle ilgili kısıtlamaların uygulanması ile Twitter'daki siber zorbalıktaki artış arasındaki güçlü bağlantının altını çizmektedir ve küresel krizlerin ortasında siber uyanıklığın artırılmasına yönelik acil ihtiyacı vurgulamaktadır.

Keywords

Covid19 Siber Zorbalık Twitter GAM

References

  • Achuthan, K., Nair, V. K., Kowalski, R., Ramanathan, S., & Raman, R. (2023). Cyberbullying research — Alignment to sustainable development and impact of COVID-19: Bibliometrics and science mapping analysis. Computers in Human Behavior, 140, 107566. https://doi.org/10.1016/j.chb.2022.107566
  • Balakrishnan, V., Khan, S., & Arabnia, H. R. (2020). Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Computers & Security, 90, 101710. https://doi.org/10.1016/j.cose.2019.101710
  • Brandt, P. T., & Sandler, T. (2012). A Bayesian Poisson vector autoregression model. Political Analysis, 20(3), 292-315.
  • Bonanno, R. A., & Hymel, S. (2013). Cyber Bullying and Internalizing Difficulties: Above and Beyond the Impact of Traditional Forms of Bullying. Journal of Youth and Adolescence, 42(5), 685-697. https://doi.org/10.1007/s10964-013-9937-1
  • Cerna, M. A. (2015). The Chinese “Togetherness-Separation” Paradox: An Analytical Approach to Understanding Chinese People’s Behavior and Its Implication to International Cooperation. International Journal of Business and Management, 10(12), 194. https://doi.org/10.5539/ijbm.v10n12p194
  • Chelmis, C., Zois, D.-S., & Yao, M. (2017). Mining Patterns of Cyberbullying on Twitter. 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 126-133. https://doi.org/10.1109/ICDMW.2017.22
  • Cheng, L., Shu, K., Wu, S., Silva, Y. N., Hall, D. L., & Liu, H. (2020). Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model (arXiv:2008.02642). arXiv. https://doi.org/10.48550/arXiv.2008.02642
  • Cuadrado-Gordillo, I., & Fernández-Antelo, I. (2016). Vulnerability and Mimicry as Predictive Axes in Cyberbullying. Journal of Interpersonal Violence, 31(1), 81-99. https://doi.org/10.1177/0886260514555128
  • Das, S., Kim, A., & Karmakar, S. (2020). Change-point analysis of cyberbullying-related twitter discussions during covid-19. arXiv preprint arXiv:2008.13613.
  • Dewani, A., Memon, M. A., & Bhatti, S. (2021). Development of computational linguistic resources for automated detection of textual cyberbullying threats in Roman Urdu language. 3 c TIC: Cuadernos de Desarrollo Aplicados a Las TIC, 10(2), 101-121.
  • Dredge, R., Gleeson, J. F. M., & de la Piedad Garcia, X. (2014). Risk Factors Associated with Impact Severity of Cyberbullying Victimization: A Qualitative Study of Adolescent Online Social Networking. Cyberpsychology, Behavior, and Social Networking, 17(5), 287-291. https://doi.org/10.1089/cyber.2013.0541
  • Dominici, F., McDermott, A., Zeger, S. L., & Samet, J. M. (2002). On the use of generalized additive models in time-series studies of air pollution and health. American journal of epidemiology, 156(3), 193-203.
  • Everbach, T., Clark, M., & Nisbett, G. S. (2018). #IfTheyGunnedMeDown: An Analysis of Mainstream and Social Media in the Ferguson, Missouri, Shooting of Michael Brown. Electronic News, 12(1), 23-41. https://doi.org/10.1177/1931243117697767
  • Feldman, L., Hart, P. S., & Milosevic, T. (2017). Polarizing news? Representations of threat and efficacy in leading US newspapers’ coverage of climate change. Public Understanding of Science, 26(4), 481-497. https://doi.org/10.1177/0963662515595348
  • Giménez Gualdo, A. M., Hunter, S. C., Durkin, K., Arnaiz, P., & Maquilón, J. J. (2015). The emotional impact of cyberbullying: Differences in perceptions and experiences as a function of role. Computers & Education, 82, 228-235. https://doi.org/10.1016/j.compedu.2014.11.013
  • Hosseinmardi, H., Mattson, S. A., Ibn Rafiq, R., Han, R., Lv, Q., & Mishra, S. (2015). Analyzing Labeled Cyberbullying Incidents on the Instagram Social Network. Içinde T.-Y. Liu, C. N. Scollon, & W. Zhu (Ed.), Social Informatics (ss. 49-66). Springer International Publishing. https://doi.org/10.1007/978-3-319-27433-1_4
  • Huang, Q., Singh, V. K., & Atrey, P. K. (2018). On cyberbullying incidents and underlying online social relationships. Journal of Computational Social Science, 1, 241-260.
  • Islam, M. M., Uddin, M. A., Islam, L., Akter, A., Sharmin, S., & Acharjee, U. K. (2020). Cyberbullying Detection on Social Networks Using Machine Learning Approaches. 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 1-6. https://doi.org/10.1109/CSDE50874.2020.9411601
  • Johanis, M. A., Bakar, A. R. A., & Ismail, F. (2020, April). Cyber-Bullying Trends Using Social Media Platform: An Analysis through Malaysian Perspectives. In Journal of Physics: Conference Series (Vol. 1529, No. 2, p. 022077). IOP Publishing.
  • Kee, D. M. H., Al-Anesi, M. A. L., & Al-Anesi, S. A. L. (2022). Cyberbullying on social media under the influence of COVID-19. Global Business and Organizational Excellence, 41(6), 11-22. https://doi.org/10.1002/joe.22175
  • López-Meneses, E., Vázquez-Cano, E., González-Zamar, M. D., & Abad-Segura, E. (2020). Socioeconomic effects in cyberbullying: Global research trends in the educational context. International journal of environmental research and public health, 17(12), 4369.
  • McHugh, M. (1997). The stress factor: Another item for the change management agenda? Journal of Organizational Change Management, 10(4), 345-362. https://doi.org/10.1108/09534819710175866
  • McHugh, M. C., Saperstein, S. L., & Gold, R. S. (2019). OMG U #Cyberbully! An Exploration of Public Discourse About Cyberbullying on Twitter. Health Education & Behavior, 46(1), 97-105. https://doi.org/10.1177/1090198118788610
  • McLoughlin, L. T., Lagopoulos, J., & Hermens, D. F. (2020). Cyberbullying and Adolescent Neurobiology. Frontiers in Psychology, 11. https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01511
  • Munira, S., Sener, I. N., & Dai, B. (2020). A Bayesian spatial Poisson-lognormal model to examine pedestrian crash severity at signalized intersections. Accident Analysis & Prevention, 144, 105679.
  • Ogolla, E., Kwanya, T., Kibe, L., Kogos, A., & Onsare, C. (2023). Curbing cyberbullying on Facebook: An analysis of mitigation strategies in universities in Kenya. Information Impact: Journal of Information and Knowledge Management, 14(1), Article 1. https://doi.org/10.4314/iijikm.v14i1.1
  • Ravindra, K., Rattan, P., Mor, S., & Aggarwal, A. N. (2019). Generalized additive models: Building evidence of air pollution, climate change and human health. Environment international, 132, 104987.
  • Saravanaraj, A., Sheeba, J. I., & Devaneyan, S. P. (2016). Automatic detection of cyberbullying from twitter. International Journal of Computer Science and Information Technology & Security (IJCSITS).
  • Sterner, G., & Felmlee, D. (2017). The Social Networks of Cyberbullying on Twitter. International Journal of Technoethics (IJT), 8(2), 1-15. https://doi.org/10.4018/IJT.2017070101
  • Taddy, M. A. (2010). Autoregressive mixture models for dynamic spatial Poisson processes: Application to tracking intensity of violent crime. Journal of the American Statistical Association, 105(492), 1403-1417.
  • Wainwright, P. E., Leatherdale, S. T., & Dubin, J. A. (2007). Advantages of mixed effects models over traditional ANOVA models in developmental studies: a worked example in a mouse model of fetal alcohol syndrome. Developmental Psychobiology: The Journal of the International Society for Developmental Psychobiology, 49(7), 664-674.
  • Wang, J., Fu, K., & Lu, C. T. (2020, December). Sosnet: A graph convolutional network approach to fine-grained cyberbullying detection. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 1699-1708). IEEE.
  • Weinberg, J., Brown, L. D., & Stroud, J. R. (2007). Bayesian forecasting of an inhomogeneous Poisson process with applications to call center data. Journal of the American Statistical Association, 102(480), 1185-1198.
  • Wisniewski, P., Jia, H., Wang, N., Zheng, S., Xu, H., Rosson, M. B., & Carroll, J. M. (2015). Resilience Mitigates the Negative Effects of Adolescent Internet Addiction and Online Risk Exposure. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 4029-4038. https://doi.org/10.1145/2702123.2702240
  • Wright, B. (2019). Weird Internet Aesthetics: Are Lo-Fi Media Inherently Revolutionary? English Honors Theses. https://creativematter.skidmore.edu/eng_stu_schol/26
  • Xu, Y., & Trzaskawka, P. (2021). Towards Descriptive Adequacy of Cyberbullying: Interdisciplinary Studies on Features, Cases and Legislative Concerns of Cyberbullying. International Journal for the Semiotics of Law - Revue Internationale de Sémiotique Juridique, 34(4), 929-943. https://doi.org/10.1007/s11196-021-09856-4
  • Verdier, G., Hilgert, N., & Vila, J. P. (2008). Optimality of cusum rule approximations in change-point detection problems: application to nonlinear state–space systems. IEEE Transactions on Information Theory, 54(11), 5102-5112.
  • Yang, F. (2021). Coping strategies, cyberbullying behaviors, and depression among Chinese netizens during the COVID-19 pandemic: A web-based nationwide survey. Journal of Affective Disorders, 281, 138-144. https://doi.org/10.1016/j.jad.2020.12.023
  • Zamba, K. D., & Hawkins, D. M. (2006). A multivariate change-point model for statistical process control. Technometrics, 48(4), 539-549.
There are 39 citations in total.

Details

Primary Language English
Subjects Sociology and Social Studies of Science and Technology, Sensory Processes, Perception and Performance
Journal Section Research Articles
Authors

Yavuz Selim Balcıoğlu 0000-0001-7138-2972

Kültigin Akçin 0000-0002-0202-8459

Early Pub Date October 26, 2023
Publication Date October 30, 2023
Published in Issue Year 2023 Volume: 20 Issue: Human Behavior and Social Institutions

Cite

APA Balcıoğlu, Y. S., & Akçin, K. (2023). Assessing the Surge in COVID-19-Related Cyberbullying on Twitter: A Generalized Additive Model Approach. OPUS Journal of Society Research, 20(Human Behavior and Social Institutions), 1014-1028. https://doi.org/10.26466/opusjsr.1349492
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