Using online social networks to track a pandemic: a systematic review
Open accessAbstract
Background The popularity and proliferation of online social networks (OSNs) have created massive social interaction among users that generate an extensive amount of data. An OSN offers a unique opportunity for studying and understanding social interaction and communication among far larger populations now more than ever before. Recently, OSNs have received considerable attention as a possible tool to track a pandemic because they can provide an almost real-time surveillance system at a less costly rate than traditional surveillance systems.Methods A systematic literature search for studies with the primary aim of using OSN to detect and track a pandemic was conducted. We conducted an electronic literature search for eligible English articles published between 2004 and 2015 using PUBMED, IEEExplore, ACM Digital Library, Google Scholar, and Web of Science. First, the articles were screened on the basis of titles and abstracts. Second, the full texts were reviewed. All included studies were subjected to quality assessment.Result OSNs have rich information that can be utilized to develop an almost real-time pandemic surveillance system. The outcomes of OSN surveillance systems have demonstrated high correlations with the findings of official surveillance systems. However, the limitation in using OSN to track pandemic is in collecting representative data with sufficient population coverage. This challenge is related to the characteristics of OSN data. The data are dynamic, large-sized, and unstructured, thus requiring advanced algorithms and computational linguistics.Conclusions OSN data contain significant information that can be used to track a pandemic. Different from traditional surveys and clinical reports, in which the data collection process is time consuming at costly rates, OSN data can be collected almost in real time at a cheaper cost. Additionally, the geographical and temporal information can provide exploratory analysis of spatiotemporal dynamics of infectious disease spread. However, on one hand, an OSN-based surveillance system requires comprehensive adoption, enhanced geographical identification system, and advanced algorithms and computational linguistics to eliminate its limitations and challenges. On the other hand, OSN is probably to never replace traditional surveillance, but it can offer complementary data that can work best when integrated with traditional data.
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Cites
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al. (2010) · Communications of the ACM · doi:10.1145/1721654.1721672 · Cited by 9,464 · see citing works
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295 · see citing works
A few useful things to know about machine learning
Domingos, P. (2012) · Communications of the ACM · doi:10.1145/2347736.2347755 · Cited by 3,326 · see citing works
The spread of behavior in an online social network experiment
Centola, D. (2010) · Science · doi:10.1126/science.1185231 · Cited by 2,692 · see citing works
Critical appraisal skills programme
Singh, J. (2013) · Journal of Pharmacology and Pharmacotherapeutics · doi:10.4103/0976-500x.107697 · Cited by 2,571 · see citing works
Porta, M. (2026) · doi:10.1093/acref/9780197663639.001.0001 · Cited by 2,175 · see citing works
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
O'Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. A. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14031 · Cited by 1,922 · see citing works
Eysenbach, G. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1157 · Cited by 1,351 · see citing works
Signorini, A., Segre, A. M., & Polgreen, P. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019467 · Cited by 1,234 · see citing works
Digital disease detection - harnessing the Web for public health surveillance
Brownstein, J. S., Freifeld, C. C., & Madoff, L. C. (2009) · New England Journal of Medicine · doi:10.1056/nejmp0900702 · Cited by 819 · see citing works
Google Trends: A Web‐Based Tool for Real‐Time Surveillance of Disease Outbreaks
Carneiro, H. A., & Mylonakis, E. E. (2009) · Clinical Infectious Diseases · doi:10.1086/630200 · Cited by 815 · see citing works
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Ghani, R., USA, K. 1. T. 1. A. S. I. C. O. K. D. A. D. M. 2. C. I., Association for Computing Machinery-Digital Library, ACM Special Interest Group on Knowledge Discovery in Data, ACM Special Interest Group on Management of Data, & SIGs, A. (2013) · doi:10.1145/2487575 · Cited by 753 · see citing works
Towards detecting influenza epidemics by analyzing Twitter messages
Culotta, A. (2010) · doi:10.1145/1964858.1964874 · Cited by 622 · see citing works
Detecting and Tracking Political Abuse in Social Media
Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Flammini, A., & Menczer, F. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14127 · Cited by 580 · see citing works
Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter
Aramaki, E., Maskawa, S., & Morita, M. (2011) · Cited by 515 · see citing works
Freifeld, C. C., Mandl, K. D., Reis, B. Y., & Brownstein, J. S. (2007) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2544 · Cited by 504 · see citing works
Salathé, M., Bengtsson, L., Bodnar, T. J., Brewer, D. D., Brownstein, J. S., Buckee, C., Campbell, E. M., Cattuto, C., Khandelwal, S., Mabry, P. L., et al. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002616 · Cited by 500 · see citing works
Broniatowski, D. A., Paul, M., & Dredze, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083672 · Cited by 474 · see citing works
Predicting Flu Trends using Twitter data
Achrekar, H., Gandhe, A., Lazarus, R. M., Yu, S.-H., & Liu, B. (2011) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2011.5928903 · Cited by 456 · see citing works
Infodemiology: tracking flu-related searches on the web for syndromic surveillance.
Eysenbach, G. (2006) · PubMed · Cited by 414 · see citing works
Daine, K., Hawton, K. E., Singaravelu, V., Stewart, A., Simkin, S., & Montgomery, P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077555 · Cited by 369 · see citing works
Influenza Forecasting with Google Flu Trends
Dugas, A. F., Jalalpour, M., Gel, Y. R., Levin, S., Torcaso, F., Igusa, T., & Rothman, R. E. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056176 · Cited by 349 · see citing works
Richiardi, L. (2014) · International Journal of Epidemiology · doi:10.1093/ije/dyu238 · Cited by 330 · see citing works
Tracking the flu pandemic by monitoring the social web
Lampos, V., & Cristianini, N. (2010) · International Workshop on Cognitive Information Processing (CIP) · doi:10.1109/cip.2010.5604088 · Cited by 309 · see citing works
Internet-based surveillance systems for monitoring emerging infectious diseases
Milinovich, G. J., Williams, G. M., Clements, A., & Hu, W. (2013) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(13)70244-5 · Cited by 306 · see citing works
Ethical challenges of big data in public health
Vayena, E., Salathé, M., Madoff, L. C., & Brownstein, J. S. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1003904 · Cited by 268 · see citing works
Velasco, E., AGHENEZA, T., Denecke, K., Kirchner, G., & Eckmanns, T. (2014) · Milbank Quarterly · doi:10.1111/1468-0009.12038 · Cited by 254 · see citing works
Visualization and analytics tools for infectious disease epidemiology: A systematic review
Carroll, L. N., Au, A., Detwiler, L. T., Fu, T., Painter, I. S., & Abernethy, N. F. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.04.006 · Cited by 251 · see citing works
Flu Detector - Tracking Epidemics on Twitter
Lampos, V., Bie, T. D., & Cristianini, N. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15939-8_42 · Cited by 248 · see citing works
Separating Fact from Fear: Tracking Flu Infections on Twitter
Lamb, A., Paul, M. J., & Dredze, M. H. (2013) · Cited by 247 · see citing works
Dengue surveillance based on a computational model of spatio-temporal locality of Twitter
Gomide, J., Veloso, A., Meira, W., Almeida, V. A. F., Benevenuto, F., Ferraz, F., & Teixeira, M. M. (2011) · ACM Web Science Conference (WebSci) · doi:10.1145/2527031.2527049 · Cited by 243 · see citing works
What can we learn about the Ebola outbreak from tweets?
Odlum, M., & Yoon, S. (2015) · American Journal of Infection Control · doi:10.1016/j.ajic.2015.02.023 · Cited by 234 · see citing works
Web Queries as a Source for Syndromic Surveillance
Hulth, A., Rydevik, G., & Linde, A. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004378 · Cited by 223 · see citing works
Real-time disease surveillance using Twitter data
Lee, K., Agrawal, A., & Choudhary, A. (2013) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487709 · Cited by 202 · see citing works
Modeling Spread of Disease from Social Interactions
Sadilek, A., Kautz, H., & Silenzio, V. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14235 · Cited by 167 · see citing works
Young, S. D., Rivers, C., & Lewis, B. L. (2014) · Preventive Medicine · doi:10.1016/j.ypmed.2014.01.024 · Cited by 166 · see citing works
Sampson, M., Cumber, J., Li, C., Pound, C., Fuller, A., & Harrison, D. M. (2013) · PeerJ · doi:10.7717/peerj.147 · Cited by 150 · see citing works
The use of social media in public health surveillance
Fung, I. C., Tse, Z. T. H., & Fu, K. (2015) · Western Pacific surveillance response journal · doi:10.5365/wpsar.2015.6.1.019 · Cited by 122 · see citing works
Proceedings of the First Workshop on Social Media Analytics
(2010) · doi:10.1145/1964858 · Cited by 108 · see citing works
Twitter mining for fine-grained syndromic surveillance
Velardi, P., Stilo, G., Tozzi, A. E., & Gesualdo, F. (2014) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2014.01.002 · Cited by 91 · see citing works
#Swineflu: Twitter Predicts Swine Flu Outbreak in 2009
Szomszor, M., Kostkova, P., & de Quincey, E. (2011) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-23635-8_3 · Cited by 89 · see citing works
Analysing Twitter and web queries for flu trend prediction
Santos, J. C. A., & Matos, S. (2014) · Theoretical Biology and Medical Modelling · doi:10.1186/1742-4682-11-s1-s6 · Cited by 79 · see citing works
Use of Hangeul Twitter to Track and Predict Human Influenza Infection
Kim, E.-K., Seok, J. H., Oh, J. S., Lee, H. W., & Kim, K. H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069305 · Cited by 78 · see citing works
Proceedings of the 4th Annual ACM Web Science Conference
(2012) · doi:10.1145/2380718 · Cited by 78 · see citing works
Early Stage Influenza Detection from TwitterPreprint
Li, J., & Cardie, C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.7340 · Cited by 59 · see citing works
Elliot, A. J., Powers, C., Thornton, A., Obi, C., Hill, C., Simms, I., Waight, P., Maguire, H., Foord, D., Povey, E., et al. (2009) · BMJ · doi:10.1136/bmj.b3403 · Cited by 52 · see citing works
Proceedings of the 3rd International Web Science Conference
Roure, D. D., & Poole, S. (2011) · doi:10.1145/2527031 · Cited by 45 · see citing works
Machine Learning and Knowledge Discovery in Databases
Buntine, W., Shawe-Taylor, J. S., Mladenić, D., & Grobelnik, M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04174-7 · Cited by 42 · see citing works
Stoove, M. A., & Pedrana, A. E. (2014) · Preventive Medicine · doi:10.1016/j.ypmed.2014.03.008 · Cited by 37 · see citing works
Epidemic Intelligence for the Crowd, by the Crowd
Diaz-Aviles, E., Stewart, A., Velasco, E., Denecke, K., & Nejdl, W. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14293 · Cited by 32 · see citing works
Belief surveillance with Twitter
Bhattacharya, S., Tran, V.-H., Srinivasan, P., & Suls, J. M. (2012) · ACM Web Science Conference (WebSci) · doi:10.1145/2380718.2380724 · Cited by 25 · see citing works
Padmanabhan, A., Wang, S., Cao, G., Hwang, M., Zhao, Y., Zhang, Z., & Gao, Y. (2013) · Extreme Science and Engineering Discovery Environment · doi:10.1145/2484762.2484821 · Cited by 23 · see citing works
Tracking Flu Epidemics Using Google Flu Trends and Particle LearningPreprint
Dukić, V., Lopes, H. F., & Polson, N. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1513705 · Cited by 18 · see citing works
Akan, O., Gerla, M., Jia, X., Weerasinghe, D., Stan, M., Shen, X. S., Sahni, S., Bellavista, P., Palazzo, S., Kobayashi, H., et al. (2009) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-00413-1 · Cited by 18 · see citing works
Detecting Flu Transmission by Social Sensor in China
Huang, J., Zhao, H. J., & Zhang, J. (2013) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/greencom-ithings-cpscom.2013.216 · Cited by 12 · see citing works
Potential of Social Media to Determine Hay Fever Seasons and Drug Efficacy
de Quincey, E. (2014) · Keele Research Repository (Keele University) · Cited by 6 · see citing works
A public health role for Internet search engine query data?
Pattie, D. C., Cox, K. L., Burkom, H. S., Lombardo, J. S., & Gaydos, J. C. (2009) · PubMed · Cited by 5 · see citing works
Infodemiology by Tweet Mining Methods
Węgrzyn-Wolska, K. M., Bougueroua, L., & Dziczkowski, G. (2013) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 1 · see citing works
Flagged first, then by citations
7 of the 68 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [45]45. B. Tograph, Y.R. Morgens, Cloud computing, Commun. ACM 51 (7) (2008).
Cited by
Sorted by citations
Psychosocial impact of COVID-19
Dubey, S., Biswas, P., Ghosh, R., Chatterjee, S., Dubey, M. J., Chatterjee, S., Lahiri, D., & Lavie, C. J. (2020) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2020.05.035 · Cited by 1,958 · see citing works
Peiffer-Smadja, N., Rawson, T. M., Ahmad, R., Buchard, A., Georgiou, P., Lescure, F.-X., Birgand, G., & Holmes, A. H. (2020) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2019.09.009 · Cited by 561 · see citing works
Alamoodi, A., Zaidan, B. B., Zaidan, A., Albahri, O., Mohammed, K., Malik, R. Q., Almahdi, E. M., Chyad, M., Tareq, Z., Albahri, A., et al. (2020) · Expert systems with applications · doi:10.1016/j.eswa.2020.114155 · Cited by 318 · see citing works
Machine learning algorithms for social media analysis: A survey
T.K., B., Annavarapu, C. S. R., & Bablani, A. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100395 · Cited by 276 · see citing works
Application of Machine Learning Methods in Mental Health Detection: A Systematic Review
Rahman, R. A., Omar, K., Noah, S. A. M., Danuri, M. S. N. M., & Al-Garadi, M. A. (2020) · IEEE Access · doi:10.1109/access.2020.3029154 · Cited by 189 · see citing works
Self-citationArtificial intelligence for modelling infectious disease epidemics
Kraemer, M. U. G., Tsui, J. L.-H., Chang, S. Y., Lytras, S., Khurana, M. P., Vanderslott, S., Bajaj, S., Scheidwasser, N., Curran-Sebastian, J. L., Semenova, E., et al. (2025) · Nature · doi:10.1038/s41586-024-08564-w · Cited by 193 · see citing works
Tran, B., Dang, A. K., Thai, P., Le, H., Le, X., Do, T., Nguyen, T., Pham, H., Phan, H., Vu, G., et al. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17103577 · Cited by 139 · see citing works
Game changers in science and technology - now and beyond
Betz, U. A. K., Arora, L., Assal, R. A., de Azevedo, H. F. Z., Baldwin, J., Becker, M. S., Bostock, S., Cheng, V. W. T., Egle, T., Ferrari, N., et al. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122588 · Cited by 146 · see citing works
Clinical text classification research trends: Systematic literature review and open issues
Mujtaba, G., Shuib, L., Idris, N., Hoo, W. L., Raj, R. G., Khowaja, K., Shaikh, K., & Nweke, H. F. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.034 · Cited by 123 · see citing works
Self-citationHernández, J. A. R., Zafra, E. M., Llor‐Esteban, B., & Jiménez‐Barbero, J. A. (2018) · The European Journal of Psychology Applied to Legal Context · doi:10.5093/ejpalc2018a11 · Cited by 132 · see citing works
Syrowatka, A., Kuznetsova, M., Alsubai, A., Beckman, A. L., Bain, P. A., Craig, K. J. T., Hu, J., Jackson, G. P., Rhee, K., & Bates, D. W. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00459-8 · Cited by 130 · see citing works
Fernández-Luque, L., & Imran, M. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.01.015 · Cited by 106 · see citing works
Conway, M., Hu, M., & Chapman, W. W. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677918 · Cited by 91 · see citing works
AsgLDP: Collecting and Generating Decentralized Attributed Graphs With Local Differential Privacy
Wei, C., Ji, S., Liu, C., Chen, W., & Wang, T. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2985524 · Cited by 88 · see citing works
Natural language processing for urban research: A systematic review
Cai, M. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06322 · Cited by 88 · see citing works
Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review
Bansal, A., Padappayil, R. P., Garg, C., Singal, A., Gupta, M., & Klein, A. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01617-3 · Cited by 87 · see citing works
The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice
Jackson, B. R., Ye, Y., Crawford, J. M., Becich, M. J., Roy, S., Botkin, J. R., de Baca, M. E., & Pantanowitz, L. (2021) · Academic Pathology · doi:10.1177/2374289521990784 · Cited by 83 · see citing works
Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis
Zadeh, A. H., Zolbanin, H. M., Sharda, R., & Delen, D. (2019) · Information Systems Frontiers · doi:10.1007/s10796-018-9893-0 · Cited by 81 · see citing works
Alshalan, R., Al-Khalifa, H. S., AlSaeed, D., Al-Baity, H. H., & Alshalan, S. (2020) · Journal of Medical Internet Research · doi:10.2196/22609 · Cited by 81 · see citing works
The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review
Alfred, R., & Obit, J. H. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e07371 · Cited by 81 · see citing works
Resume Classification System using Natural Language Processing and Machine Learning Techniques
Ali, I., Mughal, N., Khan, Z. H., Ahmed, J., & Mujtaba, G. (2022) · Mehran University Research Journal of Engineering and Technology · doi:10.22581/muet1982.2201.07 · Cited by 78 · see citing works
Self-citationUtility and potential of rapid epidemic intelligence from internet-based sources
Yan, S.-L. J., Chughtai, A. A., & MacIntyre, C. R. (2017) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2017.07.020 · Cited by 76 · see citing works
Processing Social Media Messages in Mass Emergency
Imran, M., Castillo, C., Díaz, F., & Vieweg, S. E. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3186242 · Cited by 76 · see citing works
Martínez-Millana, A., Saez-Saez, A., Tornero-Costa, R., Muscat, N. A., Traver, V., & Novillo-Ortiz, D. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104855 · Cited by 73 · see citing works
Outbreak science: recent progress in the detection and response to outbreaks of infectious diseases
Houlihan, C. F., & WHITWORTH, J. A. G. (2019) · Clinical Medicine · doi:10.7861/clinmedicine.19-2-140 · Cited by 69 · see citing works
COVID-19 on Instagram: A content analysis of selected accounts
Niknam, F., Samadbeik, M., Fatehi, F., Shirdel, M., Rezazadeh, M., & Bastani, P. (2020) · Health Policy and Technology · doi:10.1016/j.hlpt.2020.10.016 · Cited by 74 · see citing works
Artificial Intelligence for Biology
Hassoun, S., Jefferson, F., Shi, X., Stucky, B., Wang, J., & Rosa, E. (2022) · Integrative and Comparative Biology · doi:10.1093/icb/icab188 · Cited by 64 · see citing works
Chipidza, W., Akbaripourdibazar, E., Gwanzura, T., & Gatto, N. M. (2021) · Disaster Medicine and Public Health Preparedness · doi:10.1017/dmp.2021.65 · Cited by 62 · see citing works
Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews
Li, L., Novillo-Ortiz, D., Muscat, N. A., & Kostkova, P. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.645260 · Cited by 58 · see citing works
Amin, S., Uddin, M. I., Hassan, S., Khan, A., Nasser, N., Alharbi, A., & Alyami, H. (2020) · IEEE Access · doi:10.1109/access.2020.3009058 · Cited by 56 · see citing works
Social media in cardiovascular medicine: a contemporary review
Ladeiras‐Lopes, R., Baciu, L., Grapsa, J., Sohaib, A., Vidal-Pérez, R., Böhm, A., Silvola, H., Giménez, M. R., Muscoli, S., Wallner, M., et al. (2020) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaa004 · Cited by 52 · see citing works
Tweetluenza: Predicting flu trends from twitter data
Alkouz, B., Aghbari, Z. A., & Abawajy, J. (2019) · Big Data Mining and Analytics · doi:10.26599/bdma.2019.9020012 · Cited by 53 · see citing works
Mujtaba, G., Shuib, L. M., Raj, R. G., Rajandram, R., Shaikh, K., & Al-Garadi, M. A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170242 · Cited by 46 · see citing works
Self-citationPredicting the Next Influenza Pandemics
Neumann, G., & Kawaoka, Y. (2019) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiz040 · Cited by 47 · see citing works
Szmuda, T., Ali, S., Hetzger, T. V., Rosvall, P., & Słoniewski, P. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.06.028 · Cited by 46 · see citing works
Rosa, R. L., da Silva, M. J., Silva, D. H., Ayub, M. S., Carrillo, D., Nardelli, P. H. J., & Rodríguez, D. Z. (2020) · IEEE Access · doi:10.1109/access.2020.3020391 · Cited by 46 · see citing works
Al-Garadi, M. A., Yang, Y., & Sarker, A. (2022) · Healthcare · doi:10.3390/healthcare10112270 · Cited by 49 · see citing works
Self-citationVu, L. G., Le, L. K., Dam, A. V. T., Nguyen, S. H., Vu, T. M. T., Trinh, T. T. H., Do, A. L., Minh, N., Le, T. H., Latkin, C. A., et al. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.838747 · Cited by 42 · see citing works
Identifying the influential spreaders in multilayer interactions of online social networks
Al-Garadi, M. A., Varathan, K. D., Ravana, S. D., Ahmed, E., & Chang, V. I. C. (2016) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169112 · Cited by 42 · see citing works
Self-citationTsai, M. H., & Wang, Y. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18126272 · Cited by 38 · see citing works
Mujtaba, G., Shuib, L. M., Raj, R. G., Rajandram, R., & Shaikh, K. (2017) · Journal of Forensic and Legal Medicine · doi:10.1016/j.jflm.2017.07.001 · Cited by 33 · see citing works
Self-citationApplications and challenges of AI-based algorithms in the COVID-19 pandemic
Khemasuwan, D., & Colt, H. G. (2021) · BMJ Innovations · doi:10.1136/bmjinnov-2020-000648 · Cited by 31 · see citing works
Surveillance of communicable diseases using social media: A systematic review
Pilipiec, P., Samsten, I., & Bóta, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0282101 · Cited by 36 · see citing works
Deiner, M. S., Deiner, N. A., Hristidis, V., McLeod, S. D., Doan, T. A., Lietman, T. M., & Porco, T. C. (2024) · Journal of Medical Internet Research · doi:10.2196/49139 · Cited by 30 · see citing works
Use of “Social Media”—an Option for Spreading Awareness in Infection Prevention
Madhumathi, J., Sinha, R., Veeraraghavan, B., & Walia, K. (2021) · Current treatment options in infectious diseases/Current treatment options in infectious disease · doi:10.1007/s40506-020-00244-3 · Cited by 29 · see citing works
Deepluenza: Deep learning for influenza detection from Twitter
Alkouz, B., Aghbari, Z. A., Al-Garadi, M. A., & Sarker, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116845 · Cited by 29 · see citing works
Self-citationSurvey of Text-based Epidemic Intelligence
Joshi, A., Karimi, S., Sparks, R. S., Paris, C. L., & MacIntyre, C. R. (2019) · ACM Computing Surveys · doi:10.1145/3361141 · Cited by 28 · see citing works
How Jokowi Communicates with the Public During Covid-19 Crisis: An Analysis of Tweets on Twitter
Prayoga, K. (2020) · Jurnal Komunikasi Malaysian Journal of Communication · doi:10.17576/jkmjc-2020-3602-26 · Cited by 28 · see citing works
Wakamiya, S., Matsune, S., Okubo, K., & Aramaki, E. (2018) · Journal of Medical Internet Research · doi:10.2196/10450 · Cited by 27 · see citing works
Sentiment Analysis of Arabic Tweets in Smart Cities: A Review of Saudi Dialect
Alotaibi, S., Mehmood, R., & Katib, I. A. (2019) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec.2019.8795331 · Cited by 27 · see citing works
Comprehensive scoping review of health research using social media data
Taylor, J., & Pagliari, C. (2018) · BMJ Open · doi:10.1136/bmjopen-2018-022931 · Cited by 26 · see citing works
Syndromic surveillance using web data: A systematic review
Samaras, L., García‐Barriocanal, E., & Sicilia, M. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-819043-2.00002-2 · Cited by 25 · see citing works
Disease Outbreak Detection and Forecasting: A Review of Methods and Data Sources
Babanejaddehaki, G., An, A., & Papagelis, M. (2024) · ACM Transactions on Computing for Healthcare · doi:10.1145/3708549 · Cited by 24 · see citing works
Wu, G., Deng, X., & Liu, B. (2021) · Cities · doi:10.1016/j.cities.2021.103490 · Cited by 23 · see citing works
Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis
Ganser, I., Thiébaut, R., & Buckeridge, D. L. (2022) · JMIR Public Health and Surveillance · doi:10.2196/36211 · Cited by 23 · see citing works
Bashar, M. A., Nayak, R., Luong, K., & Balasubramaniam, T. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00780-w · Cited by 22 · see citing works
ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN
Bashar, M. A., & Nayak, R. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00810-2 · Cited by 34 · see citing works
Noraset, T., Chatrinan, K., Tawichsri, T., Thaipisutikul, T., & Tuarob, S. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104145 · Cited by 22 · see citing works
Chen, A., Komi, M. M., Bessler, S., Mikles, S. P., & Zhang, Y. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104439 · Cited by 21 · see citing works
Mujtaba, G., Shuib, L. M., Raj, R. G., Rajandram, R., Shaikh, K., & Al-Garadi, M. A. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.04.013 · Cited by 20 · see citing works
Self-citationAbbas, K., Nawaz, S. M. A., Jaffar, N., Soomro, F. M., Abid, K., Ahmed, M., Sayeed, K. A., Ghazanfar, S., & Qureshi, N. (2020) · Health Education Research · doi:10.1093/her/cyaa034 · Cited by 20 · see citing works
Deep learning based topic and sentiment analysis: COVID19 information seeking on social media
Bashar, M. A., Nayak, R., & Balasubramaniam, T. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00917-5 · Cited by 18 · see citing works
Şirin, H., Ahmadi, A. A., Ketrez, G., Özbeyaz, Ç., Dikmen, A. U., & Özkan, S. (2021) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.5600 · Cited by 17 · see citing works
Walsh, J., Dwumfour, C., Cave, J. A. K., & Griffiths, F. E. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01610-z · Cited by 16 · see citing works
Mujtaba, G., Shuib, L. M., Raj, R. G., Rajandram, R., & Shaikh, K. (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0191 · Cited by 16 · see citing works
Self-citationShahbaznejad, L., Navaeifar, M. R., Movahedi, F. S., Hosseinzadeh, F., Fahimzad, S. A., Serati, Z., & Rezai, M. S. (2021) · BMC Public Health · doi:10.1186/s12889-021-11039-6 · Cited by 15 · see citing works
Identifying informative tweets during a pandemic via a topic-aware neural language model
Gao, W., Li, L., Tao, X., Zhou, J., & Tao, J. (2022) · World Wide Web · doi:10.1007/s11280-022-01034-1 · Cited by 15 · see citing works
Feldhege, J., Moessner, M., Wolf, M., & Bauer, S. (2021) · Journal of Medical Internet Research · doi:10.2196/28346 · Cited by 16 · see citing works
Gamal, N., Ghoniemy, S. A., Faheem, H. M., & Seada, N. A. (2022) · IEEE Access · doi:10.1109/access.2022.3192417 · Cited by 16 · see citing works
Social Media Multidimensional Analysis for Intelligent Health Surveillance
Aramburu, M. J., Berlanga, R., & Lanza-Cruz, I. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17072289 · Cited by 12 · see citing works
Egala, S. B., Liang, D., & Boateng, D. (2022) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2022.3225182 · Cited by 12 · see citing works
Social Networks Applied to Zika and H1N1 Epidemics: A Systematic Review
de Araújo, D. H. M., de Carvalho, E. A., da Motta, C. L. R., Borges, M. R. S., Gomes, J. O., & de Carvalho, P. V. R. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-96089-0_74 · Cited by 12 · see citing works
Tripathi, A., & Bagga, T. (2020) · Indian Journal of Economics & Business · Cited by 11 · see citing works
Epidemic Alert System: A Web-based Grassroots Model
Noma‐Osaghae, E., Okokpujie, K., Ndujiuba, C. U., Okesola, O., & Okokpujie, I. P. (2018) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v8i5.pp3809-3828 · Cited by 11 · see citing works
Sentiment analysis of tweets about COVID-19 disease during pandemic
Matošević, G., & Bevanda, V. (2020) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro48935.2020.9245176 · Cited by 11 · see citing works
Concepts, objectives and analysis of public health surveillance systems
Shah, H. A., & Househ, M. S. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100136 · Cited by 14 · see citing works
Amin, S., Zeb, M. A., Alshahrani, H. M., Hamdi, M., Alsulami, M., & Shaikh, A. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.043921 · Cited by 10 · see citing works
Wakamiya, S., Morimoto, O., Omichi, K., Hara, H., Kawase, I., Koshiba, R., & Aramaki, E. (2022) · JMIR Formative Research · doi:10.2196/33941 · Cited by 10 · see citing works
Fake News Shared on WhatsApp During Covid-19: An Analysis of Groups and Statuses in Pakistan
Ahmed, M., Riaz, M. Q., Qamar, M., & Asghar, R. (2021) · Mediaobrazovanie · doi:10.13187/me.2021.1.4 · Cited by 10 · see citing works
The relationship between sentiment score and COVID-19 cases in the United States
Luu, T. (., & Follmann, R. (2022) · Journal of Information Science · doi:10.1177/01655515211068167 · Cited by 10 · see citing works
Personal health record system based on social network analysis
Tanhapour, M., Safaei, A. A., & Shakibian, H. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12910-3 · Cited by 9 · see citing works
Saha, R., Aich, S., Tripathy, S., & Kim, H. (2021) · Diagnostics · doi:10.3390/diagnostics11091604 · Cited by 8 · see citing works
The Improvement of Stress Level Detection in Twitter: Imbalance Classification Using SMOTE
Danuri, M. S. N. M., Rahman, R. A., Mohamed, I., & Amin, A. (2022) · IEEE International Conference on Computing (ICOCO) · doi:10.1109/icoco56118.2022.10031684 · Cited by 8 · see citing works
DETECTION OF SUSPICIOUS TERRORIST EMAILS USING TEXT CLASSIFICATION: A REVIEW
Mujtaba, G., Shuib, L. M., Raj, R. G., & Gunalan, R. (2018) · Malaysian Journal of Computer Science · doi:10.22452/mjcs.vol31no4.3 · Cited by 8 · see citing works
Self-citationShah, H. A., & Househ, M. (2024) · BMJ Public Health · doi:10.1136/bmjph-2023-000374 · Cited by 7 · see citing works
Yang, Y., Tsao, S., Basri, M. A., Chen, H., & Butt, Z. A. (2023) · Studies in health technology and informatics · doi:10.3233/shti230290 · Cited by 7 · see citing works
Cai, M., Li, J., Nali, M. C., & Mackey, T. K. (2021) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops50388.2021.9473830 · Cited by 7 · see citing works
Estimating Clustering Coefficient of Multiplex Graphs with Local Differential Privacy
Liu, Z., Xu, H., Huang, L., & Yang, W. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86137-7_42 · Cited by 7 · see citing works
Can Citizen Science Contribute to Avian Influenza Surveillance?
Saavedra, I., Rabadán‐González, J., Aragonés, D., & Figuerola, J. (2023) · Pathogens · doi:10.3390/pathogens12091183 · Cited by 6 · see citing works
Ng, J. Y., Abdelkader, W., & Lokker, C. (2022) · BMC Complementary Medicine and Therapies · doi:10.1186/s12906-022-03586-1 · Cited by 6 · see citing works
Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review
Gabarrón, E., Rivera-Romero, O., Miron‐Shatz, T., Grainger, R., & Denecke, K. (2021) · Yearbook of Medical Informatics · doi:10.1055/s-0041-1726486 · Cited by 7 · see citing works
A renal genetics clinic – model for our times
Robinson, C., Sandford, R. N., Wiseman, O. J., Sarker, A., & Karet, F. E. (2019) · Clinical Medicine · doi:10.7861/clinmedicine.19-2-s140 · Cited by 5 · see citing works
The role of artificial intelligence in predicting disease outbreaks: A multidisciplinary approach
Alfardan, A. A. I., Alharbi, R. F. R., Alrammaal, W., Alharbi, F. S., Almotairi, M. S., Almutairi, M. N., Almutairi, N. S., Hazazi, M. M. M., Alanazi, M. M. M., & Alharbi, F. M. M. (2024) · International Journal of Health Sciences · doi:10.53730/ijhs.v8ns1.15327 · Cited by 5 · see citing works
Al-Garadi, M. A., Lenoue-Newton, M. L., Matheny, M. E., McPheeters, M. L., Whitaker, J. M., Deere, J., McLemore, M. F., Westerman, D. M., Khan, M. S., Hernández‐Muñoz, J. J., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/71113 · Cited by 5 · see citing works
Self-citationKnowledge, Curiosity, Communication Channels and Panic during COVID-19 Movement Control Order
Salman, A. (2021) · International Journal of Media and Communication Research · doi:10.25299/ijmcr.v2i1.6205 · Cited by 5 · see citing works
Impact of Covid-19 Infodemic on the Global Picture
Rudra, T., & Kautish, S. (2021) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-68936-0_16 · Cited by 5 · see citing works
DeSAN: De-anonymization against Background Knowledge in Social Networks
Desai, N., & Das, M. L. (2021) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/icics52457.2021.9464573 · Cited by 5 · see citing works
Sun, H., Zhang, Y., Gao, G., & Wu, D. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.958835 · Cited by 5 · see citing works
Survey: Analysis of Security Issues on Social Media using Data Science techniques
Krishna, Y. V., Jahnavi, G., Tharun, M., Yegineti, S. G., Raja, G., & Suneetha, B. (2023) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict57646.2023.10134391 · Cited by 5 · see citing works
Spontaneously Generated Online Patient Experience of Modafinil: A Qualitative and NLP Analysis
Walsh, J., Cave, J. A. K., & Griffiths, F. E. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.598431 · Cited by 5 · see citing works
- Authors
- Al-garadi, M. A., Khan, M. S., Varathan, K. D., Mujtaba, G., & Al-Kabsi, A. M.
- Affiliations
- University of Malaya (MY)
- Corresponding author
- Al-garadi, Mohammed Ali
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2016-08
- Volume
- 62
- Pages
- 1-11
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2405148542
- Semantic Scholar
- e9aa85ab55e806006e12d33bab3e0833b6fbaf2e
- Cited by
- 166
- References
- 68
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 17 · 2023: 13 · 2022: 27 · 2021: 37 · 2020: 30 · 2019: 12 · 2018: 12 · 2017: 6 · 2016: 4
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Malaya (RP028D-14AET)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Social Sciences › Health › Social Media in Health Education
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Keywords
- social networking sites, pandemic surveillance, infectious disease surveillance, real-time surveillance, social media data, computational linguistics, data representativeness
- MeSH terms
- Communicable Diseases, Humans, Population Surveillance, Surveys and Questionnaires, Social Support, Pandemics, Social Networking, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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