Document Type : Original/Review Paper


1 Department of Computer Engineering, Hamedan University of Technology, Hamedan, Iran.

2 Department of Biomedical Engineering, Hamedan University of Technology, Hamedan, Iran.

3 Society of Rural Social Development, University of Tehran, Tehran, Iran.


During the COVID-19 crisis, we face a wide range of thoughts, feelings, and behaviors on social media that play a significant role in spreading information regarding COVID-19. Trustful information, together with hopeful messages, could be used to control people's emotions and reactions during pandemics. This study examines Iranian society's resilience in the face of the Corona crisis and provides a strategy to promote resilience in similar situations. It investigates posts and news related to the COVID-19 pandemic in Iran, to determine which messages and references have caused concern in the community, and how they could be modified? and also which references were the most trusted publishers? Social network analysis methods such as clustering have been used to analyze data. In the present work, we applied a two-stage clustering method constructed on the self-organizing map and K-means. Because of the importance of social trust in accepting messages, This work examines public trust in social posts. The results showed trust in the health-related posts was less than social-related and cultural-related posts. The trusted posts were shared on Instagram and news sites. Health and cultural posts with negative polarity affected people's trust and led to negative emotions such as fear, disgust, sadness, and anger. So, we suggest that non-political discourses be used to share topics in the field of health.


[1] A. Abd-Alrazaq, D. Alhuwail, M. Househ, M. Hamdi, and Z. Shah, "Top concerns of tweeters during the COVID-19 pandemic: infoveillance study" Journal of medical Internet research, vol. 22, no. 4, pp. e19016, 2020.
[2] A. R. Ahmad and H. R. Murad, "The impact of social media on panic during the COVID-19 pandemic in Iraqi Kurdistan: online questionnaire study" Journal of Medical Internet Research, vol. 22, no. 5, p. e19556, 2020.
[3] X. Ji, S. A. Chun, and J. Geller, "Monitoring public health concerns using twitter sentiment classifications" in 2013 IEEE International Conference on Healthcare Informatics, 2013, pp. 335-344.
[4] M. Smith, D. A. Broniatowski, M. J. Paul, and M. Dredze, "Towards real-time measurement of public epidemic awareness: Monitoring influenza awareness through twitter" in AAAI spring symposium on observational studies through social media and other human-generated content, 2016.
[5] W. Sherchan, S. Nepal, and C. Paris, "A survey of trust in social networks" Computing Surveys, vol. 45, no. 4, pp. 1-33, 2013.
[6] L. Mollema et al., "Disease detection or public opinion reflection? Content analysis of tweets, other social media, and online newspapers during the measles outbreak in The Netherlands in 2013" Journal of medical Internet research, vol. 17, no. 5, pp. e128, 2015.
[7] A. J. Lazard, E. Scheinfeld, J. M. Bernhardt, G. B. Wilcox, and M. Suran, "Detecting themes of public concern: a text mining analysis of the Centers for Disease Control and Prevention's Ebola live Twitter chat" American journal of infection control, vol. 43, no. 10, pp. 1109-1111, 2015.
[8] T. Tran and K. Lee, "Understanding citizen reactions and Ebola-related information propagation on social media" in 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2016, pp. 106-111.
[9] M. Miller, T. Banerjee, R. Muppalla, W. Romine, and A. Sheth, "What are people tweeting about Zika? An exploratory study concerning its symptoms, treatment, transmission, and prevention" JMIR public health and surveillance, vol. 3, no. 2, p. e38, 2017.
[10] L.-C. Chen, C.-M. Lee, and M.-Y. Chen, "Exploration of social media for sentiment analysis using deep learning" Soft Computing, vol. 24, no. 11, pp. 8187-8197, 2020.
[11] G. Li and F. Liu, "Sentiment analysis based on clustering: a framework in improving accuracy and recognizing neutral opinions" Applied intelligence, vol. 40, no. 3, pp. 441-452, 2014.
[12] V. K. Vijayan, K. Bindu, and L. Parameswaran, "A comprehensive study of text classification algorithms" in 2017 International Conference on Advances in Computing, Communications and Informatics, 2017, pp. 1109-1113.
[13] B. Liu, E. Blasch, Y. Chen, D. Shen, and G. Chen, "Scalable sentiment classification for big data analysis using naive bayes classifier" in 2013 IEEE international conference on big data, 2013, pp. 99-104.
[14] E. Boiy, P. Hens, K. Deschacht, and M. F. Moens "Automatic Sentiment Analysis in On-line Text" in ELPUB, 2007, pp. 349-360.
[15] W. Ramadhan, S. A. Novianty, and S. C. Setianingsih, "Sentiment analysis using multinomial logistic regression" in 2017 International Conference on Control, Electronics, Renewable Energy and Communications, 2017, pp. 46-49.
[16] K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, and D. Brown, "Text classification algorithms: A survey" Information, vol. 10, no. 4, pp. 150, 2019.
[17] L. Nemes and A. Kiss, "Social media sentiment analysis based on COVID-19" Journal of Information and Telecommunication, pp. 1-15, 2020.
[18] G. Li and F. Liu, "Application of a clustering method on sentiment analysis" Journal of Information Science, vol. 38, no. 2, pp. 127-139, 2012.
[19] S. Wu, Y. Liu, J. Wang, and Q. Li, "Sentiment analysis method based on Kmeans and online transfer learning" Cmccomputers Materials and Continua, vol. 60, no. 3, pp. 1207-1222, 2019.
[20] F. Kaveh-Yazdy and S. Zarifzadeh, "Track Iran's national COVID-19 response committee’s major concerns using two-stage unsupervised topic modeling" International journal of medical informatics, vol. 145, pp. 104309, 2021.
[21] K. E. Matsa and E. Shearer, "News use across social media platforms 2018" Pew Research Center, vol. 10, 2018.
[22] P. Hitlin and K. Olmstead, "The science people see on social media. Pew Research Center" ed, 2018.
[23] X. Ye, S. Li, X. Yang, and C. Qin, "Use of social media for the detection and analysis of infectious diseases in China" International Journal of Geo-Information, vol. 5, no. 9, pp. 156, 2016.
[24] I. C.-H. Fung et al., "Pedagogical Demonstration of twitter data analysis: A case study of world AIDS day, 2014" Data, vol. 4, no. 2, pp. 84, 2019.
[25] E. H.-J. Kim, Y. K. Jeong, Y. Kim, K. Y. Kang, and M. Song, "Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news" Journal of Information Science, vol. 42, no. 6, pp. 763-781, 2016.
[26] J. Samuel, M. Rahman, G. Ali, Y. Samuel, and A. Pelaez, "Feeling Like it is Time to Reopen Now? COVID-19 New Normal Scenarios based on Reopening Sentiment Analytics" Nawaz and Samuel, Yana and Pelaez, Alexander, Feeling Like it is Time to Reopen Now, 2020.
[27] A. Mosam, S. Goldstein, A. Erzse, A. Tugendhaft, and K. Hofman, "Building trust during COVID 19: Value-driven and ethical priority-setting" SAMJ: South African Medical Journal, vol. 110, no. 6, pp. 1-4, 2020.
[28] A. Oksanen, M. Kaakinen, R. Latikka, I. Savolainen, N. Savela, and A. Koivula, "Regulation and trust: 3-month follow-up study on COVID-19 mortality in 25 European countries" JMIR Public Health and Surveillance, vol. 6, no. 2, pp. e19218, 2020.
[29] L. Li et al., "Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo" IEEE Transactions on Computational Social Systems, vol. 7, no. 2, pp. 556-562, 2020.
[30] C. E. Lopez, M. Vasu, and C. Gallemore, "Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset" arXiv preprint arXiv:2003.10359, 2020.
[31] Z.-Y. Tao et al., "Nature and diffusion of COVID-19–related oral health information on Chinese social media: analysis of tweets on weibo" Journal of Medical Internet Research, vol. 22, no. 6, pp. e19981, 2020.
[32] E. Massaad and P. Cherfan, "Social media data analytics on telehealth during the COVID-19 pandemic" Cureus, vol. 12, no. 4, 2020.
[33] V. P. Vinogradac, J. P. Vukičević, and I. C. Mraović, "Value system as a factor of young people's trust in education during the Covid-19 pandemic in three countries of southeast europe" Društvene i humanističke studije, vol. 5, no. 3 (12), pp. 331-354, 2020.
[34] D. H. Balog-Way and K. A. McComas, "COVID-19: Reflections on trust, tradeoffs, and preparedness" Journal of Risk Research, vol. 23, no. 7-8, pp. 838-848, 2020.
[35] A. Deslatte, "The erosion of trust during a global pandemic and how public administrators should counter it" The American Review of Public Administration, vol. 50, no. 6-7, pp. 489-496, 2020.
[36] P. B. Forsyth, C. M. Adams, and W. K. Hoy, "Collective trust" Why schools can’t improve, 2011.
[37] D. H. Balog-Way and K. A. McComas, "COVID-19: Reflections on trust, tradeoffs, and preparedness" Journal of Risk Research, pp. 1-11, 2020.
[38] D. Pastor-Escuredo and C. Tarazona, "Characterizing information leaders in Twitter during COVID-19 crisis" arXiv preprint arXiv:2005.07266, 2020.
[39] J. Zarocostas, "How to fight an infodemic," The Lancet, vol. 395, no. 10225, pp. 676, 2020.
[40] L. Bode and E. K. Vraga, "See something, say something: Correction of global health misinformation on social media" Health communication, vol. 33, no. 9, pp. 1131-1140, 2018.
[41] P. M. Waszak, W. Kasprzycka-Waszak, and A. Kubanek, "The spread of medical fake news in social media–the pilot quantitative study" Health policy and technology, vol. 7, no. 2, pp. 115-118, 2018.
[42] L. Singh et al., "A first look at COVID-19 information and misinformation sharing on Twitter," arXiv preprint arXiv:2003.13907, 2020.
[43] P. Wicke and M. M. Bolognesi, "Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter" arXiv preprint arXiv:2004.06986, 2020.
[44] S. Llewellyn, "Covid-19: how to be careful with trust and expertise on social media," BMJ, vol. 368, 2020.
[45] R. Kouzy et al., "Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter" Cureus, vol. 12, no. 3, 2020.
[46] F. Pierri and S. Ceri, "False news on social media: a data-driven survey" ACM Sigmod Record, vol. 48, no. 2, pp. 18-27, 2019.
[47] B. Ghanem, P. Rosso, and F. Rangel, "An emotional analysis of false information in social media and news articles" ACM Transactions on Internet Technology, vol. 20, no. 2, pp. 1-18, 2020.
[48] K. Popat, S. Mukherjee, A. Yates, and G. Weikum, "Declare: Debunking fake news and false claims using evidence-aware deep learning" arXiv preprint arXiv:1809.06416, 2018.
[49] N. Ruchansky, S. Seo, and Y. Liu, "Csi: A hybrid deep model for fake news detection" in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017, pp. 797-806.
[50] R. Plutchik, "A general psychoevolutionary theory of emotion" in Theories of emotion: Elsevier, 1980, pp. 3-33.
[51] V. Khachidze, T. Wang, S. Siddiqui, V. Liu, S. Cappuccio, and A. Lim, "Contemporary research on E-business technology and strategy" in Conference proceedings iCETS, 2012, pp. 43.
[52] Y.-C. Liu, M. Liu, and X.-L. Wang, "Application of self-organizing maps in text clustering: a review chapter", 2012.
[53] Y. P. Raykov, A. Boukouvalas, F. Baig, and M. A. Little, "What to do when k-means clustering fails: a simple yet principled alternative algorithm" PloS one, vol. 11, no. 9, pp. e0162259, 2016.
[54] C. Patil and I. Baidari, "Estimating the optimal number of clusters k in a dataset using data depth" Data Science and Engineering, vol. 4, no. 2, pp. 132-140, 2019.
[55] S. H. Chuah, S. Gächter, R. Hoffmann, and J. H. Tan, "Religion, discrimination and trust across three cultures" European Economic Review, vol. 90, pp. 280-301, 2016.
[56] J. C. Bezdek, R. Ehrlich, and W. Full, "FCM: The fuzzy c-means clustering algorithm" Computers and geosciences, vol. 10, no. 2-3, pp. 191-203, 1984.
[57] A. Lakizadeh and E. Moradizadeh, "Text sentiment classification based on separate embedding of aspect and context" Journal of AI and Data Mining, vol. 10, no.1 , pp. 139-149, 2022.