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Monitoring the Relationship between Social Network Status and Influenza Based on Social Media Data

Published online by Cambridge University Press:  18 September 2023

Qi Yan ,
Siqing Shan ,
Baishang Zhang ,
Weize Sun ,
Menghan Sun ,
Yiting Luo ,
Feng Zhao  and
Xiaoshuang Guo
Qi Yan
Affiliation:
Management School, Tianjin Normal University, Tianjin, China
Siqing Shan*
Affiliation:
School of Economics and Management, Beihang University, Beijing, China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
Baishang Zhang
Affiliation:
Development Research Center of State Administration for Market Regulation of the PR China, Beijing, China
Weize Sun
Affiliation:
School of Economics and Management, Beihang University, Beijing, China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
Menghan Sun
Affiliation:
School of Economics and Management, Beihang University, Beijing, China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
Yiting Luo
Affiliation:
School of Economics and Management, Beihang University, Beijing, China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
Feng Zhao
Affiliation:
School of Economics and Management, Beihang University, Beijing, China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
Xiaoshuang Guo
Affiliation:
School of Economics and Management, Beihang University, Beijing, China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
*
Corresponding author: Siqing Shan; Email: shansiqing@buaa.edu.cn.
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Abstract

Background:

This article aims to analyze the relationship between user characteristics on social networks and influenza.

Methods:

Three specific research questions are investigated: (1) we classify Weibo updates to recognize influenza-related information based on machine learning algorithms and propose a quantitative model for influenza susceptibility in social networks; (2) we adopt in-degree indicator from complex networks theory as social media status to verify its coefficient correlation with influenza susceptibility; (3) we also apply the LDA topic model to explore users’ physical condition from Weibo to further calculate its coefficient correlation with influenza susceptibility. From the perspective of social networking status, we analyze and extract influenza-related information from social media, with many advantages including efficiency, low cost, and real time.

Results:

We find a moderate negative correlation between the susceptibility of users to influenza and social network status, while there is a significant positive correlation between physical condition and susceptibility to influenza.

Conclusions:

Our findings reveal the laws behind the phenomenon of online disease transmission, and providing important evidence for analyzing, predicting, and preventing disease transmission. Also, this study provides theoretical and methodological underpinnings for further exploration and measurement of more factors associated with infection control and public health from social networks.

Keywords

social network statusphysical conditioninfluenzapublic healthinformation model

Information

Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 17 , 2023 , e490
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health

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References

Potter, CW. A history of influenza. J Appl Microbiol. 2001;91:572-579. doi: 10.1046/j.1365-2672.2001.01492.x CrossRefGoogle ScholarPubMed
World Health Organization. Global influenza strategy 2019-2030. Geneva. 2019. Licence: CC BY-NC-SA 3.0 IGO. Cataloguing-in-Publication (CIP) data. Accessed July 22, 2023. http://apps.who.int/iris Google Scholar
Donnelly, CA, Ghani, AC, Leung, GM, et al. Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. Lancet. 2003;361(9371):1761-1766. doi: 10.1016/s0140-6736(03)13410-1 CrossRefGoogle ScholarPubMed
Domínguez-Cherit, G, Lapinsky, SE, Macias, AE, et al. Critically ill patients with 2009 influenza a(H1N1) in Mexico. JAMA. 2009;302(17):1880-1887. doi: 10.1001/jama.2009.1536 CrossRefGoogle ScholarPubMed
Jia, JS, Lu, X, Yuan, Y, et al. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature. 2020;582(7812):389-394. doi: 10.1038/s41586-020-2284-y CrossRefGoogle ScholarPubMed
Hershfield, HE, Scheibe, S, Sims, TL, et al. When feeling bad can be good: mixed emotions benefit physical health across adulthood. Soc Psychol Personal Sci. 2013;4(1):54-61. doi: 10.1177/1948550612444616 CrossRefGoogle ScholarPubMed
Karademas, EC, Dimitraki, G, Papastefanakis, E, et al. Emotion regulation contributes to the well-being of patients with autoimmune diseases through illness-related emotions: a prospective study. J Health Psychol. 2020;25(13-14):2096-2105. doi: 10.1177/1359105318787010 CrossRefGoogle Scholar
Tyra, AT, Griffin, SM, Fergus, TA, et al. Individual differences in emotion regulation prospectively predict early COVID-19 related acute stress. J Anxiety Disord. 2021;81:102411. doi: 10.1016/j.janxdis.2021.102411 CrossRefGoogle ScholarPubMed
Edo-Osagie, O, De La Iglesia, B, Lake, I, et al. A scoping review of the use of Twitter for public health research. Comput Biol Med. 2020;122:103770. doi: 10.1016/j.compbiomed.2020.103770 CrossRefGoogle ScholarPubMed
Nguyen, T, Larsen, M, O’Dea, B, et al. Using spatiotemporal distribution of geocoded Twitter data to predict US county-level health indices. Future Gener Comput Syst. 2020;110:620-628, doi: 10.1016/j.future.2018.01.014 CrossRefGoogle Scholar
Charles-Smith, LE, Reynolds, TL, Cameron, MA, et al. Using social media for actionable disease surveillance and outbreak management: a systematic literature review. PLoS One. 2015;10(10):e0139701. doi: 10.1371/journal.pone.0139701 CrossRefGoogle ScholarPubMed
Shan, S, Lin, X. Research on emergency dissemination models for social media based on information entropy. Enterp Inf Syst. 2018;12:888-909. doi: 10.1080/17517575.2017.1293300 CrossRefGoogle Scholar
Alessa, A, Faezipour, M. A review of influenza detection and prediction through social networking sites. Theor Biol Med Model. 2018;15(1):2, doi: 10.1186/s12976-017-0074-5 CrossRefGoogle ScholarPubMed
Scanfeld, D, Scanfeld, V, Larson, EL. Dissemination of health information through social networks: Twitter and antibiotics. Am J Infect Control. 2010;38(3):182-188. doi: 10.1016/j.ajic.2009.11.004 CrossRefGoogle ScholarPubMed
Aramaki, E, Sachiko, M, Mizuki, M. Twitter catches the flu: detecting influenza epidemics using factuality analysis. Proceedings of the 2011 Conference on empirical methods in natural language processing, Edinburgh, Scotland, UK, July 2011.Google Scholar
Ahmed, W, Bath, PA, Sbaffi, L, et al. Novel insights into views towards H1N1 during the 2009 Pandemic: a thematic analysis of Twitter data. Heatlth Info Libr J. 2019;36(1):60-72. doi: 10.1111/hir.12247 CrossRefGoogle ScholarPubMed
Yousefinaghani, S, Dara, R, Poljak, Z, et al. The assessment of Twitter’s potential for outbreak detection: avian influenza case study. Sci Rep. 2019;9(1):18147. doi: 10.1038/s41598-019-54388-4 CrossRefGoogle ScholarPubMed
Paul, MJ, Dredze, M, Broniatowski, D. Twitter improves influenza forecasting. PLoS Curr. 2014;6:ecurrents.outbreaks.90b9ed0f59bae4ccaa683a39865d9117. doi: 10.1371/currents.outbreaks.90b9ed0f59bae4ccaa683a39865d9117 Google ScholarPubMed
Aiello, AE, Renson, A, Zivich, PN. Social media- and internet-based disease surveillance for public health. Annu Rev Public Health. 2020;41:101-118. doi: 10.1146/annurev-publhealth-040119-094402 CrossRefGoogle ScholarPubMed
Lampos, V, Miller, AC, Crossan, S, et al. Advances in nowcasting influenza-like illness rates using search query logs. Sci Rep. 2015;5:12760. doi: 10.1038/srep12760 CrossRefGoogle ScholarPubMed
Masri, S, Jia, J, Li, C, et al. Use of Twitter data to improve Zika virus surveillance in the United States during the 2016 epidemic. BMC Public Health. 2019;19(1):761. doi: 10.1186/s12889-019-7103-8 CrossRefGoogle ScholarPubMed
Samaras, L, García-Barriocanal, E, Sicilia, M-A. Comparing social media and Google to detect and predict severe epidemics. Sci Rep. 2020;10(1):4747. doi: 10.1038/s41598-020-61686-9 CrossRefGoogle ScholarPubMed
Hoebel, J, Maske, UE, Zeeb, H, et al. Social inequalities and depressive symptoms in adults: the role of objective and subjective socioeconomic status. PLoS One. 2017;12(1):e0169764. doi: 10.1371/journal.pone.0169764 CrossRefGoogle ScholarPubMed
Huynh, VW, Chiang, JJ. Subjective social status and adolescent health: the role of stress and sleep. Youth Soc. 2018;50(7):926-946. doi: 10.1177/0044118X16646028 CrossRefGoogle Scholar
Stringhini, S, Carmeli, C, Jokela, M, et al. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1·7 million men and women. Lancet. 2017;389(10075):1229-1237.CrossRefGoogle ScholarPubMed
Fournier, MA. Dimensions of human hierarchy as determinants of health and happiness. Curr Opin Psychol. 2020;33:110-114. doi: 10.1016/j.copsyc.2019.07.014 CrossRefGoogle ScholarPubMed
Uecker, JE, Wilkinson, LR. College selectivity, subjective social status, and mental health in young adulthood. Soc Ment Health. 2020;10(3):257-275. doi: 10.1177/2156869319869401 CrossRefGoogle Scholar
Euteneuer, F, Schäfer, SJ, Neubert, M, et al. Subjective social status and health-related quality of life—a cross-lagged panel analysis. Health Psychol. 2021:40(1):71-76. doi: 10.1037/hea0001051 CrossRefGoogle ScholarPubMed
McMaughan, DJ, Oloruntoba, O, Smith, ML. Socioeconomic status and access to healthcare: interrelated drivers for healthy aging. Front Public Health. 2020;8:231. doi: 10.3389/fpubh.2020.00231 CrossRefGoogle ScholarPubMed
Kivimäki, M, Batty, GD, Pentti, J, et al. Association between socioeconomic status and the development of mental and physical health conditions in adulthood: a multi-cohort study. Lancet Public Health. 2020;5(3):e140-e149. doi: 10.1016/S2468-2667(19)30248-8 CrossRefGoogle ScholarPubMed
Wanberg, CR, Csillag, B, Douglass, RP, et al. Socioeconomic status and well-being during COVID-19: a resource-based examination. J Appl Psychol. 2020;105(12):1382. doi: 10.1037/apl0000831 CrossRefGoogle ScholarPubMed
Peverill, M, Dirks, MA, Narvaja, T, et al. Socioeconomic status and child psychopathology in the United States: a meta-analysis of population-based studies. Clin Psychol Rev. 2021;83:101933. doi: 10.1016/j.cpr.2020.101933 CrossRefGoogle ScholarPubMed
Tung, J, Barreiro, LB, Johnson, ZP, et al. Social environment is associated with gene regulatory variation in the rhesus macaque immune system. Proc Natl Acad Sci U S A. 2012;109(17):6490-6495. doi: 10.1073/pnas.1202734109 CrossRefGoogle ScholarPubMed
Hassan Zadeh, A, Zolbanin, HM, Sharda, R, et al. Social media for nowcasting flu activity: spatio-temporal big data analysis. Inf Syst Front. 2019;21:743-760. doi: 10.1007/s10796-018-9893-0 CrossRefGoogle Scholar
Perez-Rodríguez, G, Pérez-Pérez, M, Fdez-Riverola, F, et al. Mining the sociome for health informatics: analysis of therapeutic lifestyle adherence of diabetic patients in Twitter. Futur Gener Comp Syst. 2020;110:214-232. doi: 10.1016/j.future.2020.04.025 CrossRefGoogle Scholar
Murayama, T, Shimizu, N, Fujita, S, et al. Predicting regional influenza epidemics with uncertainty estimation using commuting data in Japan. PLoS One. 2021;16(4):e0250417. doi: 10.1371/journal.pone.0250417 CrossRefGoogle ScholarPubMed
Qin, Z, Ronchieri, E. Exploring pandemics events on Twitter by using sentiment analysis and topic modelling. Appl Sci (Basel). 2022;12(23):21. doi: 10.3390/app122311924.Google Scholar
Wang, H, Xiong, L, Wang, C, et al. Understanding Chinese mobile social media users’ communication behaviors during public health emergencies. J Risk Res. 2022;25(7):874-891. doi: 10.1080/13669877.2022.2049621 CrossRefGoogle Scholar
Sapolsky, RM. Social status and health in humans and other animals. Annu Rev Anthropol. 2004;33:393-418. doi: 10.1146/annurev.anthro.33.070203.144000 CrossRefGoogle Scholar
Okamoto, J, Johnson, CA, Leventhal, A, et al. Social network status and depression among adolescents: an examination of social network influences and depressive symptoms in a Chinese sample. Res Hum Dev. 2011;8:67-88. doi: 10.1080/15427609.2011.549711 CrossRefGoogle Scholar
Chang, AC. Chapter 5 - Machine and deep learning. In: Chang, AC, ed. Intelligence-Based Medicine. Academic Press; 2020:67-140. doi: 10.1016/B978-0-12-823337-5.00005-6 CrossRefGoogle ScholarPubMed
Lee, C, Lee, GG. Information gain and divergence-based feature selection for machine learning-based text categorization. Inf Process Manag. 2006;42:155-165. doi: 10.1016/j.ipm.2004.08.006 CrossRefGoogle Scholar
Jones, KS. Index term weighting. Inf Storage Retrieva. 1973;9(11):619-633. doi10.1016/0020-0271(73)90043-0 CrossRefGoogle Scholar
Aizawa, A. An information-theoretic perspective of tf-idf measures. Inf Process Manag. 2003;39:45-65, doi: 10.1016/s0306-4573(02)00021-3 CrossRefGoogle Scholar
Santos, JC, Matos, S. Analysing Twitter and web queries for flu trend prediction. Theor Biol Med Model. 2014;11:11. doi: 10.1186/1742-4682-11-s1-s6 CrossRefGoogle ScholarPubMed
Shan, SQ, Yan, Q, Wei, YG. Infectious or recovered? Optimizing the infectious disease detection process for epidemic control and prevention based on social media. Int J Environ Res Public Health. 2020;17(18):25. doi: 10.3390/ijerph17186853 CrossRefGoogle ScholarPubMed
Uddin, S, Hossain, L, Wigand, RT. New direction in degree centrality measure: towards a time-variant approach. Int J Inf Technol Decis Mak. 2014;13:865-878. doi: 10.1142/s0219622014500217 CrossRefGoogle Scholar
Gomez, D, Gonzalez-Aranguena, E, Manuel, C, et al. Centrality and power in social networks: a game theoretic approach. Math Soc Sci. 2003;46(1):27-54. doi: 10.1016/s0165-4896(03)00028-3 CrossRefGoogle Scholar
Alshahrani, M, Zhu, F, Sameh, A, et al. Efficient algorithms based on centrality measures for identification of top-K influential users in social networks. Inf Sci. 2020;527:88-107. doi: 10.1016/j.ins.2020.03.060 CrossRefGoogle Scholar
Lee, J, Lee, Y, Oh, SM, et al. Betweenness centrality of teams in social networks. Chaos. 2021. doi: 10.1063/5.0056683 CrossRefGoogle ScholarPubMed
Freeman, LC. Centrality in social networks’ conceptual clarification. Soc Netw. 1979;1:215-239. doi: 10.1016/0378-8733(78)90021-7 CrossRefGoogle Scholar
Carboni, JL. Social network analysis: methods and applications. J Publ Adm Res Theory. 2015;25:981-987. doi: 10.1093/jopart/muu083 CrossRefGoogle Scholar
Cadini, F, Zio, E, Petrescu, CA. Using centrality measures to rank the importance of the components of a complex network infrastructure. In: Setola, R, Geretshuber, S, eds. Critical Information Infrastructures Security. Springer-Verlag; 2009:155-167.Google Scholar
Likert, R, Roslow, S, Murphy, G. A simple and reliable method of scoring the Thurstone Attitude Scales. Pers Psychol. 2006;46(3):689-690. doi: 10.1111/j.1744-6570.1993.tb00893.x CrossRefGoogle Scholar
Xiang, L. Recommendation System Practice. Posts and Telecom Press; 2012:238-240.Google Scholar
Spearman, C. The proof and measurement of association between two things. By C. Spearman, 1904. Am J Psychol. 1987;100(3-4):441-471. doi: 10.2307/1422689 CrossRefGoogle Scholar
Heinen, A, Valdesogo, A. Spearman rank correlation of the bivariate Student t and scale mixtures of normal distributions. J Multivar Anal. 2020;179:11. doi: 10.1016/j.jmva.2020.104650 CrossRefGoogle Scholar
Mikolov, T, Chen, K, Corrado, G, et al. Efficient estimation of word representations in vector space. 2013:arXiv preprint. doi: 10.48550/arXiv.1301.3781 CrossRefGoogle Scholar
Xu, Y, Wang, C, Dan, Z, et al. Deep recurrent neural network and data filtering for rumor detection on Sina Weibo. Symmetry (Basel). 2019;11:11. doi: 10.3390/sym11111408 Google Scholar
Conway, M, Doan, S, Kawazoe, A, et al. Classifying disease outbreak reports using n-grams and semantic features. Int J Med Inform. 2009;78(12):E47-E58. doi: 10.1016/j.ijmedinf.2009.03.010 CrossRefGoogle ScholarPubMed
Metz, CE. Basic principles of ROC analysis. Semin Nucl Med. 1978;8:283-298. doi: 10.1016/s0001-2998(78)80014-2 CrossRefGoogle ScholarPubMed
Aslan, S. A novel TCNN-Bi-LSTM deep learning model for predicting sentiments of tweets about COVID-19 vaccines. Concurr Comput. 2022;34(28):e7387. doi: 10.1002/cpe.7387 CrossRefGoogle ScholarPubMed
Narra, M, Umer, M, Sadiq, S, et al. Selective feature sets based fake news detection for COVID-19 to manage infodemic. IEEE Access. 2022;10:98724-98736. doi: 10.1109/access.2022.3206963 CrossRefGoogle Scholar
Raj, C, Meel, P. ARCNN framework for multimodal infodemic detection. Neural Netw. 2022;146:36-68. doi: 10.1016/j.neunet.2021.11.006 CrossRefGoogle ScholarPubMed
Shan, SQ, Zhao, F, Wei, YG, et al. Disaster management 2.0: a real-time disaster damage assessment model based on mobile social media data: a case study of Weibo (Chinese Twitter). Saf Sci. 2019;115:393-413. doi: 10.1016/j.ssci.2019.02.029 CrossRefGoogle Scholar
Chen, SJ, Mao, J, Li, G, et al. Uncovering sentiment and retweet patterns of disaster-related tweets from a spatiotemporal perspective - a case study of Hurricane Harvey. Telemat Inform. 2020;47:18. doi: 10.1016/j.tele.2019.101326 CrossRefGoogle Scholar
Woo, H, Cho, Y, Shim, E, et al. Estimating influenza outbreaks using both search engine query data and social media data in South Korea. J Med Internet Res. 2016;18:11. doi: 10.2196/jmir.4955 CrossRefGoogle ScholarPubMed
Gao, YZ, Wang, SW, Padmanabhan, A, et al. Mapping spatiotemporal patterns of events using social media: a case study of influenza trends. Int J Geogr Inf Sci. 2018;32:425-449. doi: 10.1080/13658816.2017.1406943 CrossRefGoogle Scholar
Yoo, S, Kim, D, Yang, SM, et al. Real-time disease detection and analysis system using social media contents. Int J Web Grid Serv. 2020;16:22-38.CrossRefGoogle Scholar
Ma, JW, Yang, Y, Wilson, JAJ. A window to the ideal self: a study of UK Twitter and Chinese Sina Weibo selfie-takers and the implications for marketers. J Bus Res. 2017;74:139-142. doi: 10.1016/j.jbusres.2016.10.025 CrossRefGoogle Scholar