Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derleme

Yıl 2021, , 439 - 449, 27.07.2021

Doğan Küçük , Nursal Arıcı , Emine Ela Küçük

https://doi.org/10.28948/ngumuh.778948

Öz

Yaygın hastalıklar ve salgınlar gibi halk sağlığını durumlarının otomatik olarak belirlenerek takip edilmesi, güncel ve önemli bir araştırma problemidir. Günümüzde, sosyal medya metinleri analiz edilerek halk sağlığı takibi yapılabilmekte, toplumun sağlıkla ilgili eğilimleri ve algıları belirlenebilmektedir. Literatürde bu konularda gerçekleştirilmiş çalışmaların sayısı da hızla artış göstermektedir. Bu çalışmamızda, sosyal medya üzerinde halk sağlığı ile ilgili içerikleri tespit eden ve halk sağlığı takibi yapan çalışmaların güncel bir derlemesi sunulmaktadır. Söz konusu çalışmalar; salgınlar, hastalıklar, tıbbi gelişmeler, aşılar ve tamamlayıcı/alternatif tıp gibi halk sağlığı ile ilgili tüm konuları hedef alabilmektedir. Derlememizde, sosyal medyada otomatik halk sağlığı takibi konusundaki güncel çalışmalar alt konularına göre sınıflandırılarak sunulmuş olup, ilgili dijital kaynakları listelenmiş ve ayrıca ileri çalışma konularına yer verilmiştir. Derlememizin, sağlık bilişimi konusunda hem teorik hem de uygulamaya yönelik önemli bir kaynak olarak ilgili araştırmacı ve uzmanlara hizmet etmesi beklenmektedir.

Anahtar Kelimeler

sağlık bilişimi, sosyal medya analizi, makine öğrenmesi, derin öğrenme, halk sağlığı takibi

Automatic public health monitoring on social media: A recent survey

Öz

Automatic detection and monitoring of public health events and phenomena, like common diseases and epidemics, is an important research problem. Today, public health monitoring can be performed automatically on social media and health-related trends and perceptions of the society can be determined by analyzing social media texts. Related studies performed on these topics are increasing. In this study, a recent survey of the studies that detect public health related content on social media and that perform public health monitoring, is presented. Related studies can target at any public health related topics including epidemics, diseases, medical advances, vaccines, and complementary/alternative medicine. In our survey, those studies on automatic public health monitoring on social media are presented after they are categorized by their sub-topics, related digital resources are listed, and additionally, future research topics are included. It is expected that our survey will serve as an important theoretical and application-oriented resource for related researchers and experts.

Anahtar Kelimeler

Public health monitoring, Health informatics, Social media analysis, Machine learning, Deep learning

Kaynakça

• Z. J. Yao, J. Bi, and Y. X. Chen, Applying deep learning to individual and community health monitoring data: a survey, International Journal of Automation and Computing, 15 (6), 643-55, 2018. https://doi.org/10.1007/s11633-018-1136-9
• D. Lazer, R. Kennedy, G. King, and A. Vespignani, The parable of Google Flu: Traps in big data analysis, Science, 343 (6176), 1203-1205, 2014. https://doi.org/10.1126/science.1248506
• Y. Pershad, P. T. Hangge, H. Albadawi, and R. Oklu, Social medicine: Twitter in healthcare, Journal of Clinical Medicine, 7 (6), 121, 2018. https://doi.org/10.3390/jcm7060121
• E. Chen, K. Lerman, and E. Ferrara, #Covid-19: The first public coronavirus Twitter dataset, arXiv preprint arXiv:2003.07372, 2020.
• M. Aydogan, and A. Sener, An Artificial Intelligence Application in Health Developed on Covid-19 Documents, Journal of Health, Medicine and Nursing, 75, 58-66, 2020. https://doi.org/10.7176/JHMN/75-08
• R. Thiébaut, and F. Thiessard, Artificial Intelligence in Public Health and Epidemiology, Yearbook of Medical Informatics, 27 (01), 207-10, 2018. https://doi.org/10.1055/s-0038-1667082
• L. Zhou, D. Zhang, C. C. Yang, and Y. Wang, Harnessing social media for health information management, Electronic Commerce Research and Applications, 27, 139-51, 2018. https://doi.org/10.1016/j.elerap.2017.12.003
• P. Velardi, G. Stilo, A. E. Tozzi, and F. Gesualdo, Twitter mining for fine-grained syndromic surveillance, Artificial Intelligence in Medicine, 61 (3), 153-63, 2014. https://doi.org/10.1016/j.artmed.2014.01.002
• E. E. Küçük, K. Yapar, D. Küçük, and D. Küçük, Ontology-based automatic identification of public health-related Turkish tweets, Computers in Biology and Medicine, 83, 1-9, 2017. https://doi.org/10.1016/j.compbiomed.2017.02.001
• A. Culotta, Estimating county health statistics with Twitter, SIGCHI Conference on Human Factors in Computing Systems, pp. 1335-1344, 2014. https://doi.org/10.1145/2556288.2557139
• J. M. Kapp, B. Hensel, and K. T. Schnoring, Is Twitter a forum for disseminating research to health policy makers?, Annals of Epidemiology, 25 (12), 883-7, 2015. https://doi.org/10.1016/j.annepidem.2015.09.002
• N. Phan, D. Dou, B. Piniewski, and D. Kil, Social restricted Boltzmann machine: Human behavior prediction in health social networks, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 424-431, 2015. https://doi.org/10.1145/2808797.2809307
• P. Grover, A. K. Kar, and G. Davies, Technology enabled Health–Insights from Twitter analytics with a socio-technical perspective, International Journal of Information Management, 43, 85-97, 2018. https://doi.org/10.1016/j.ijinfomgt.2018.07.003
• I. C. H. Fung, Z. T. H. Tse, and K. W. Fu, The use of social media in public health surveillance, Western Pacific Surveillance and Response Journal: WPSAR, 6 (2), 3, 2015. https://doi.org/10.5365/WPSAR.2015 .6.1.019
• R. Fang, S. Pouyanfar, Y. Yang, S. C. Chen, and S. S. Iyengar, Computational health informatics in the big data age: a survey,ACM Computing Surveys (CSUR), 49 (1), 1-36, 2016. https://doi.org/10.1145/2932707
• A. Nikfarjam, Health information extraction from social media, Ph. D. thesis, Arizona State University, Tempe, AZ, 2016.
• D. Ravì, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, and G. Z. Yang, Deep learning for health informatics, IEEE Journal of Biomedical and Health Informatics, 21 (1), 4-21, 2017. https://doi.org/10.1109/JBHI.2016.2636665
• I. Goodfellow, Y. Bengio and A. Courville. Deep Learning. Cambridge: MIT Press, 2016.
• D. Küçük, and N. Arıcı, Doğal dil işlemede derin öğrenme uygulamaları üzerine bir literatür çalışması, Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, 2 (2), 76-86, 2018.
• A. Joshi, S. Karimi, R. Sparks, C. Paris, and C. R. Macintyre, Survey of text-based epidemic intelligence: a computational linguistics perspective,ACM Computing Surveys (CSUR),52 (6), 1-19, 2019. https://doi.org/10.1145/3361141
• K. M. Rabarison, M. A. Croston, N. K. Englar, C. L. Bish, S. M. Flynn, and C. C. Johnson, Measuring audience engagement for public health Twitter chats: insights from# LiveFitNOLA, JMIR Public Health and Surveillance, 3 (2), 34, 2017.
• J. P. Guidry, Y. Jin, C. A. Orr, M. Messner, and S. Meganck, Ebola on Instagram and Twitter: How health organizations address the health crisis in their social media engagement, Public Relations Review, 43 (3), 477-486, 2017. https://doi.org/10.1016/j.pubrev.2017.04.009
• S. E. Jordan, S. E. Hovet, I. C. H. Fung, H. Liang, K. W. Fu, and Z. T. H. Tse, Using Twitter for public health surveillance from monitoring and prediction to public response, Data, 4 (1), 6, 2019. https://doi.org/10.3390/ data4010006
• J. Parker, Y. Wei, A. Yates, O. Frieder, and N. Goharian, A framework for detecting public health trends with Twitter, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 556-563, 2013 https://doi.org/10.1145/2492517.2492544
• L. Zhao, J. Chen, F. Chen, W. Wang, C. T. Lu, and N. Ramakrishnan, Simnest: Social media nested epidemic simulation via online semi-supervised deep learning, IEEE International Conference on Data Mining, pp. 639-648, 2015. https://doi.org/10.1109/ICDM.2015.39.
• S. Choi, J. Lee, M. G. Kang, H. Min, Y. S. Chang, and S. Yoon, Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks, Methods, 129, 50-59, 2017. https://doi.org/10.1016/j.ymeth.2017.07.027
• A. Sarker et al., Data and systems for medication-related text classification and concept normalization from Twitter: Insights from the Social Media Mining for Health (SMM4H)-2017 shared task, Journal of the American Medical Informatics Association, 25 (10), pp. 1274-1283, 2018. https://doi.org/10.1093/jamia/ocy114
• E. Tutubalina, Z. Miftahutdinov, S. Nikolenko, and V. Malykh, Medical concept normalization in social media posts with recurrent neural networks,Journal of Biomedical Informatics, 84, 93-102, 2018. https://doi.org/10.1016/j.jbi.2018.06.006
• T. L. Wiemken et al., Methods for computational disease surveillance in infection prevention and control: statistical process control versus Twitter's anomaly and breakout detection algorithms,American Journal of Infection Control, 46 (2), 124-132, 2018. https://doi.org/10.1016/j.ajic.2017.08.005
• M. A. Magumba, P. Nabende, and E. Mwebaze, Ontology boosted deep learning for disease name extraction from Twitter messages, Journal of Big Data, 5 (1), 31, 2018. https://doi.org/10.1186/s40537-018-0139-2
• K. Jiang, S. Feng, Q. Song, R. A. Calix, M. Gupta, and G. R. Bernard, Identifying tweets of personal health experience through word embedding and LSTM neural network, BMC Bioinformatics, 19 (8), 210, 2018. https://doi.org/10.1186/s12859-018-2198-y
• S. Doan, E. W. Yang, S. S. Tilak, P. W. Li, D. S. Zisook, and M. Torii, Extracting health-related causality from Twitter messages using natural language processing, BMC Medical Informatics and Decision Making, 19 (3), 79, 2019. https://doi.org/10.1186/s12911-019-0785-0
• O. Şerban, N. Thapen, B. Maginnis, C. Hankin, and V. Foot, Real-time processing of social media with SENTINEL: a syndromic surveillance system incorporating deep learning for health classification, Information Processing & Management, 56 (3), 1166-1184, 2019. https://doi.org/10.1016/j.ipm.2018.04.011
• A. Khatua, A. Khatua, and E. Cambria, A tale of two epidemics: Contextual Word2Vec for classifying Twitter streams during outbreaks, Information Processing & Management, 56 (1), 247-257, 2019. https://doi.org/10.1016/j.ipm.2018.10.010
• A. Calamusa, S. Tardelli, M. Avvenuti, S. Cresci, I. Federigi, M. Tesconi, M. Verani, and A. Carducci. Twitter monitoring evidence of Covid-19 infodemic in Italy, European Journal of Public Health, 30 (5), ckaa165-066, 2020.
• S. C. Guntuku, G. Sherman, D. C. Stokes, A. K. Agarwal, E. Seltzer, R. M. Merchant, L. H. Ungar. Tracking mental health and symptom mentions on Twitter during COVID-19, Journal of General Internal Medicine, 35 (9), 2798-800, 2020. https://doi.org/10.1007/s11606-020-05988-8
• K. Jahanbin and V. Rahmanian. Using Twitter and web news mining to predict COVID-19 outbreak. Asian Pacific Journal of Tropical Medicine, 13 (8), 378-380, 2020. https://doi.org/10.4103/1995-7645.279651
• S. Kaur, P. Kaul, and P. M. Zadeh. Monitoring the Dynamics of Emotions during COVID-19 Using Twitter Data, Procedia Computer Science, 177, 423-430, 2020. https://doi.org/10.1016/j.procs.2020.10.056
• J. Xue, J. Chen, R. Hu, C. Chen, C. Zheng, Y. Su and T. Zhu. Twitter Discussions and Emotions about the COVID-19 Pandemic: Machine Learning Approach, Journal of Medical Internet Research, 22 (11), e20550, 2020. https://doi.org/10.2196/20550
• Y. Zhang, H. Lyu, Y. Liu, X. Zhang, Y. Wang, and J. Luo. Monitoring Depression Trend on Twitter during the COVID-19 Pandemic, arXiv preprint arXiv:2007.00228, 2020.
• K. Lee, A. Agrawal, and A. Choudhary, Real-time disease surveillance using Twitter data: demonstration on flu and cancer, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1474-1477, 2013. https://doi.org/10.1145/2487575.2487709
• M. A. Stoové, and A. E. Pedrana, Making the most of a brave new world: Opportunities and considerations for using Twitter as a public health monitoring tool, Preventive Medicine, 63, 109-111, 2014. https://doi.org/10.1016/j.ypmed.2014.03.008
• M. Santillana, A. T. Nguyen, M. Dredze, M. J. Paul, E. O. Nsoesie, and J. S. Brownstein, Combining search, social media, and traditional data sources to improve influenza surveillance, PLoS Computational Biology, 11 (10), 2015. https://doi.org/10.1371/journal.pcbi.1004513
• K. Byrd, A. Mansurov, and O. Baysal, Mining Twitter data for influenza detection and surveillance, International Workshop on Software Engineering in Healthcare Systems, pp. 43-49, 2016. https://doi.org/10.1145/2897683.2897693
• C. Comito, A. Forestiero, and C. Pizzuti, Twitter-based influenza surveillance: An analysis of the 2016-2017 and 2017-2018 seasons in Italy, International Database Engineering & Applications Symposium, pp. 175-182, 2018. https://doi.org/10.1145/3216122.3216128
• S. Wakamiya, Y. Kawai, and E. Aramaki, Twitter-based influenza detection after flu peak via tweets with indirect information: text mining study, JMIR Public Health and Surveillance, 4 (3), e65, 2018.
• S. Wakamiya, M. Morita, Y. Kano, T. Ohkuma, and E. Aramaki, Tweet classification toward Twitter-based disease surveillance: New data, methods, and evaluations, Journal of Medical Internet Research, 21 (2), e12783, 2019. https://doi.org/10.2196/12783
• G. Gkotsis et al., Characterisation of mental health conditions in social media using informed deep learning, Scientific Reports, 7, 45141, 2017. https://doi.org/10.1038/srep45141
• J. Du, S. Michalska, S. Subramani, H. Wang, and Y. Zhang, Neural attention with character embeddings for hay fever detection from Twitter, Health Information Science and Systems, 7 (1), 21, 2019. https://doi.org/10.1007/s13755-019-0084-2
• A. B. Abacha, M. F. M. Chowdhury, A. Karanasiou, Y. Mrabet, A. Lavelli, and P. Zweigenbaum, Text mining for pharmacovigilance: Using machine learning for drug name recognition and drug–drug interaction extraction and classification, Journal of Biomedical Informatics, 58, 122-132, 2015. https://doi.org/10.1016/j.jbi.2015.09.015
• R. L. Kendra, S. Karki, J. L. Eickholt, and L. Gandy, Characterizing the discussion of antibiotics in the Twittersphere: What is the bigger picture?, Journal of Medical Internet Research, 17 (6), e154, 2015. https://doi.org/10.2196/jmir.4220
• A. Sarker et al., Utilizing social media data for pharmacovigilance: a review, Journal of Biomedical Informatics, 54, 202-212, 2015. https://doi.org/10.1016/j.jbi.2015.02.004
• A. Nikfarjam, A. Sarker, K. O’Connor, R. Ginn, and G. Gonzalez, Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features, Journal of the American Medical Informatics Association, 22 (3), 671-681, 2015. https://doi.org/10.1093/jamia/ocu041
• L. Xia, G. A. Wang, and W. Fan, A deep learning based named entity recognition approach for adverse drug events identification and extraction in health social media, International Conference on Smart Health, pp. 237-248, 2017. https://doi.org/10.1007/978-3-319-67964-8_23
• A. Cocos, A. G. Fiks, and A. J. Masino, Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts, Journal of the American Medical Informatics Association, 24 (4), 813-821, 2017. https://doi.org/10.1093/jamia/ocw180
• L. Zhang, M. Hall, and D. Bastola, Utilizing Twitter data for analysis of chemotherapy, International Journal of Medical Informatics, 120, 92-100, 2018. https://doi.org/10.1016/j.ijmedinf.2018.10.002
• B. Fan, W. Fan, and C. Smith, Adverse drug event detection and extraction from open data: A deep learning approach, Information Processing & Management, 57 (1), 102131, 2020. https://doi.org/10.1016/j.ipm.2019.102131
• M. Myslín, S. H. Zhu, W. Chapman, and M. Conway, Using Twitter to examine smoking behavior and perceptions of emerging tobacco products, Journal of Medical Internet Research, 15 (8), e174, 2013. https://doi.org/10.2196/jmir.2534
• M. A. Moreno, A. Arseniev-Koehler, D. Litt, and D. Christakis, Evaluating college students' displayed alcohol references on Facebook and Twitter, Journal of Adolescent Health, 58 (5), 527-532, 2016. https://doi.org/10.1016/j.jadohealth.2016.01.005
• J. B. Unger et al., Talking about tobacco on Twitter is associated with tobacco product use, Preventive Medicine, 114, 54-56, 2018. https://doi.org/10.1016/ j.ypmed.2018.06.006
• B. Pang, and L. Lee, Opinion mining and sentiment analysis, Foundations and Trends in Information Retrieval, 2 (1–2), 1-135, 2008.
• S. Mohammad, S. Kiritchenko, P. Sobhani, X. Zhu, and C. Cherry, SemEval-2016 task 6: Detecting stance in tweets, International Workshop on Semantic Evaluation (SemEval-2016), pp. 31-41, 2016.
• E. K. Seltzer, E. Horst-Martz, M. Lu, and R. M. Merchant, Public sentiment and discourse about Zika virus on Instagram, Public Health, 150, 170-175, 2017. https://doi.org/10.1016/j.puhe.2017.07.015
• S. Zhang, L. Qiu, F. Chen, W. Zhang, Y. Yu, and N. Elhadad, We make choices we think are going to save us: Debate and stance identification for online breast cancer CAM discussions, International Conference on World Wide Web Companion, pp. 1073-1081, 2017. https://doi.org/10.1145/3041021.3055134
• J. Du et al., Leveraging deep learning to understand health beliefs about the Human Papillomavirus Vaccine from social media, NPJ Digital Medicine, 2 (1), 27, 2019. https://doi.org/10.1038/s41746-019-0102-4
• D. A. Lindberg, B. L. Humphreys, and A. T. McCray, The unified medical language system, Yearbook of Medical Informatics, 2 (01), 41-51, 1993. https://doi.org/10.1055/s-0038-1634945
• C. E. Lipscomb, Medical subject headings (MeSH), Bulletin of the Medical Library Association, 88 (3), 265, 2000.
• A. A. Çobaner, S. Köksoy. Sağlık alanında sosyal medyanın kullanımı: Twitter’da sağlık mesajları, Akademik Konferans Bildirileri, 899-906, 2014.
• A. Bilgiç, S. S. Akyüz. Türkiye’de Covid-19 pandemisi döneminde Sağlık Bakanı Fahrettin Koca’nın sosyal medya kullanımı: Twitter paylaşımları içerik analizi, Gaziantep Üniversitesi Sosyal Bilimler Dergisi, 19(Covid-19 Özel Sayı), 230-243, 2020.