Social Media-Based Surveillance Systems for Healthcare using Machine Learning


  •   Dr. Chetanpal Singh

  •   Dr. Rahul Thakkar

  •   Jatinder Warraich


One of the most popular domains that have caught the attention of researchers is real-time surveillance in the health and informatics segment. Many initiatives have been discovered due to this real-time surveillance surrounding public health informatics. Real-time surveillance in the health and informatics field has used the information from social media to predict the outbreak of diseases as well as to look after the diseases. There is no doubt in the fact that the availability of the data from social media in the recent past, especially the data from Twitter, has offered the researchers real-time syndromic surveillance in making quick analyses and conclusions in investigating the disease outbreak. The paper will get to know about the recent work of machine learning trends and text classification that has been utilized by the surveillance system by using the data from social media in the field of healthcare. Apart from this, the paper has also discussed the various limitations and challenges by taking into account the future direction that can be considered in this domain further.

Keywords: Disease Prediction, Health Prediction, Instagram, Machine Learning, Outbreak, Social Media, Surveillance Systems, Twitter.


Du LJ, Tang L. Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data. Vaccines, 2022; 10(103): 1-11.

Aiello E, Renson A, Zivich PN. Social Media and Internet-Based Disease Surveillance for Public Health. Annu. Rev. Public Health, 2020; 41: 101-118.

Gupta, Katarya R. Social media based surveillance systems for healthcare using machine learning: A systematic review. Journal of Biomedical Informatics, 2020; 108: 103500.

Hossein Abad ZS, Kline A, Sultana M, Noaeen M. Digital public health surveillance: a systematic scoping review. NPJ Digital Medicine, 2021; 4(41): 1-13.

Chiolero, Buckeridge D. Glossary for public health surveillance in the age of data science. Journal of Epidemiology Community Health, 2020; 74(7): 612-616.

Bates M. Tracking Disease: Digital Epidemiology Offers New Promise in Predicting Outbreaks. IEEE Pulse, 2017; 8: 18-22.

Calix R, Gupta R, Gupta M, Jiang K. Deep gramulator: Improving precision in the classification of personal health-experience tweets with deep learning. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2017.

Mike, Daniel C. Social media, big data, and mental health: current advances and ethical implications. Current Opinion in Psychology, 2016; 9: 77-82.

Sousa L, de Mello R, Cedrim D, Garcia A, Missier P, Uchôa A, Oliveira A, Romanovsky A. VazaDengue: An information system for preventing and combating mosquito-borne diseases with social networks. Information Systems, 2018; 75: 26-42.

Ji X, Chun S, Geller J. Monitoring public health concerns using twitter sentiment classifications. 2013 IEEE International Conference on Healthcare Informatics, IEEE; 2013.

Lee K, Agrawal A, Choudhary A. Mining social media streams to improve public health allergy surveillance. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2015.

Nargund K, Natarajan S. Public health allergy surveillance using micro-blogs. 2016 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI; 2016.

Yang N, Cui X, Hu C, Zhu W, Yang C. Chinese social media analysis for disease surveillance. 2014 International Conference on Identification, Information and Knowledge in the Internet of Things, IEEE; 2014.

Jain V, Kumar S. Effective surveillance and predictive mapping of mosquito-borne diseases using social media. J. Comput. Sci., 2018; 25: 406-415.

Espina K, Regina M, Estuar J. Infodemiology for Syndromic Surveillance of Dengue and Typhoid Fever in the Philippines. Proc. Comput. Sci., 2017; 121: 554-561.

Du J, Tang L, Xiang Y, Zhi D, Xu J, Song H, Tao C. Public perception analysis of tweets during the 2015 measles outbreak: Comparative study using convolutional neural network models. J. Med. Internet Res., 2018; 20: 1-11.

Jiang K, Gupta R, Gupta M, Calix R, Bernard G. Identifying Personal Health Experience Tweets with Deep Neural Networks* HHS Public Access. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2017.

Kumar V, Kumar S. An Effective Approach to Track Levels of Influenza-A (H1N1) Pandemic in India Using Twitter. Procedia Computer Science, 2015; 70: 801-807.

Calix R, Gupta R, Gupta M, Jiang K. Deep gramulator: Improving precision in the classification of personal health-experience tweets with deep learning. Proc.-2017 IEEE Int. Conf. Bioinforma. Biomed. BIBM; January 2017.

Korkontzelos, Piliouras D, Dowsey A, Ananiadou S. Boosting drug named entity recognition using an aggregate classifier. Artif. Intell. Med., 2015; 65: 145-153.

Zhang W, Ram S, Burkart M, Pengetnze Y. Extracting signals from social media for chronic disease surveillance. Proceedings of the 6th International Conference on Digital Health Conference; 2016.

Mowery. Twitter Influenza Surveillance: Quantifying Seasonal Misdiagnosis Patterns and their Impact on Surveillance Estimates. Online J Public Heal. Inf., 2016; 8(3).

Nsoesie E, Flor L, Hawkins J, Maharana A, Skotnes T, Marinho F, Brownstein J. Social media as a sentinel for disease surveillance: what does sociodemographic status have to do with it? PLoS Currents, 2016; 8.

Dai X, Bikdash M, Meyer B. From social media to public health surveillance: Word embedding based clustering method for twitter classification. SoutheastCon 2017, IEEE; 2017.

Sousa, de Mello R, Cedrim D, Garcia A, Missier P, Uchôa A, Oliveira A, Romanovsky A. VazaDengue: An information system for preventing and combating mosquito-borne diseases with social networks. Inf. Syst., 2018; 75: 26-42.

Chaudhary S, Naaz S. Use of big data in computational epidemiology for public health surveillance. 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN), IEEE; 2017.

Bosley J, Zhao N, Hill S, Shofer F, Asch D, Becker L, Merchant R. Decoding twitter: Surveillance and trends for cardiac arrest and resuscitation communication. Resuscitation, 2013; 84: 206-212.

Stefanidis, Vraga E, Lamprianidis G, Radzikowski J, Delamater P, Jacobsen K, Pfoser D, et al. Zika in Twitter: temporal variations of locations, actors, and concepts. JMIR public health and surveillance, 2017; 3(2): e6925.

Rudra, Sharma A, Ganguly N, Imran M. Classifying information from microblogs during epidemics. Proceedings of the 2017 international conference on digital health; 2017.

Edd, Rn S. What can we learn about the Ebola outbreak from tweets? Am. J. Infect. Control., 2015; 43: 563-571.

Kwak H, Lee C, Park H, Moon S. What is Twitter, a Social Network or a News Media? Arch. Zootec., 2011; 11: 297-300.

Systrom K. Strengthening our commitment to safety and kindness for 800 million. Instagram Press, 2017. Accessed 9 March 2022. [Internet]. Available:

Guidry J, Jin Y, Orr C, Messner M, Meganck S. Ebola on Instagram and Twitter: How health organizations address the health crisis in their social media engagement. Public Relat. Rev., 2017; 43: 477-486.

Tang L, Bie B, Zhi D. Tweeting about measles during stages of an outbreak: A semantic network approach to the framing of an emerging infectious disease. American Journal of Infection Control, 2018; 46(12), 1375–1380.

Kostkova P, Szomszor M, St. Luis C. #swineflu: The Use of Twitter as an EarlyWarning and Risk Communication. ACM Transactions on Management Information Systems, 2014; 5(2), 1–25.

Zhou, Ye J, Feng Y, Tuberculosis surveillance by analyzing google trends. IEEE Trans. Biomed. Eng., 2011; 58: 2247-2254.

Yom-Tov E. Ebola data from the Internet: An opportunity for syndromic surveillance or a news event? Proceedings of the 5th international conference on digital health; 2015.

Young S, Mercer N, Weiss R, Torrone E, Aral S. Using social media as a tool to predict syphilis. Prev. Med. (Baltim), 2018; 109: 58-61.

Nolasco D, Oliveira J. Subevents Detection through Topic Modeling in Social Media Posts. Future Generation Computer Systems, 2018; 93: 290-303.

Thapen, Simmie D, Hankin C, Gillard J. DEFENDER: detecting and forecasting epidemics using novel data-analytics for enhanced response. PloS one, 2016; 11(5): e0155417.

Mckee R. Ethical issues in using social media for health and health care research. Health Policy (New. York), 2013; 110: 298-301.

Blouin-Genest G, Miller A. The politics of participatory epidemiology: Technologies, social media and influenza surveillance in the US. Heal. Policy Technol., 2017; 6: 192-197.

Bodnar T, Salathé M. Validating models for disease detection using twitter. Proceedings of the 22nd International Conference on World Wide Web; 2013.

Charles-Smith L, Reynolds T, Cameron M, Conway M, Lau E, Olsen J, Pavlin J, et al. Using social media for actionable disease surveillance and outbreak management: A systematic literature review. PloS one, 2015; 10(10): e0139701.

Strekalova Y. Emergent health risks and audience information engagement on social media. Am. J. Infect. Control., 2016; 44: 363-365.

Limsopatham, Collier N. Towards the semantic interpretation of personal health messages from social media. Proceedings of the ACM First International Workshop on Understanding the City with Urban Informatics; 2015.

Kou Y, Gui X, Chen Y, Pine K. Conspiracy Talk on Social Media: Collective Sensemaking during a Public Health Crisis. Proc. ACM Human-Computer Interact, 2017; 1: 1-21.

Cataldi J, Dempsey A, O'Leary S. Measles, the media, and MMR: Impact of the 2014–15 measles outbreak. Vaccine, 2016; 34: 6375-6380.


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How to Cite
Singh, D.C., Thakkar, D.R. and Warraich, J. 2022. Social Media-Based Surveillance Systems for Healthcare using Machine Learning. European Journal of Engineering and Technology Research. 7, 6 (Nov. 2022), 21–28. DOI: