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Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derleme

Year 2021, Volume: 10 Issue: 2, 439 - 449, 27.07.2021
https://doi.org/10.28948/ngumuh.778948

Abstract

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.

References

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Automatic public health monitoring on social media: A recent survey

Year 2021, Volume: 10 Issue: 2, 439 - 449, 27.07.2021
https://doi.org/10.28948/ngumuh.778948

Abstract

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.

References

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  • 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
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  • 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
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There are 69 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Computer Engineering
Authors

Doğan Küçük 0000-0001-5265-3263

Nursal Arıcı 0000-0002-4505-1341

Emine Ela Küçük 0000-0002-3805-9767

Publication Date July 27, 2021
Submission Date August 13, 2020
Acceptance Date January 6, 2021
Published in Issue Year 2021 Volume: 10 Issue: 2

Cite

APA Küçük, D., Arıcı, N., & Küçük, E. E. (2021). Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derleme. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 10(2), 439-449. https://doi.org/10.28948/ngumuh.778948
AMA Küçük D, Arıcı N, Küçük EE. Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derleme. NOHU J. Eng. Sci. July 2021;10(2):439-449. doi:10.28948/ngumuh.778948
Chicago Küçük, Doğan, Nursal Arıcı, and Emine Ela Küçük. “Sosyal Medyada Otomatik Halk sağlığı Takibi: Güncel Bir Derleme”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10, no. 2 (July 2021): 439-49. https://doi.org/10.28948/ngumuh.778948.
EndNote Küçük D, Arıcı N, Küçük EE (July 1, 2021) Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derleme. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10 2 439–449.
IEEE D. Küçük, N. Arıcı, and E. E. Küçük, “Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derleme”, NOHU J. Eng. Sci., vol. 10, no. 2, pp. 439–449, 2021, doi: 10.28948/ngumuh.778948.
ISNAD Küçük, Doğan et al. “Sosyal Medyada Otomatik Halk sağlığı Takibi: Güncel Bir Derleme”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10/2 (July 2021), 439-449. https://doi.org/10.28948/ngumuh.778948.
JAMA Küçük D, Arıcı N, Küçük EE. Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derleme. NOHU J. Eng. Sci. 2021;10:439–449.
MLA Küçük, Doğan et al. “Sosyal Medyada Otomatik Halk sağlığı Takibi: Güncel Bir Derleme”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 10, no. 2, 2021, pp. 439-4, doi:10.28948/ngumuh.778948.
Vancouver Küçük D, Arıcı N, Küçük EE. Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derleme. NOHU J. Eng. Sci. 2021;10(2):439-4.

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