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A framework for detecting public health trends with Twitter

Published: 25 August 2013 Publication History

Abstract

Traditional public health surveillance requires regular clinical reports and considerable effort by health professionals to analyze data. Therefore, a low cost alternative is of great practical use. As a platform used by over 500 million users worldwide to publish their ideas about many topics, including health conditions, Twitter provides researchers the freshest source of public health conditions on a global scale. We propose a framework for tracking public health condition trends via Twitter. The basic idea is to use frequent term sets from highly purified health-related tweets as queries into a Wikipedia article index -- treating the retrieval of medically-related articles as an indicator of a health-related condition. By observing fluctuations in frequent term sets and in turn medically-related articles over a series of time slices of tweets, we detect shifts in public health conditions and concerns over time. Compared to existing approaches, our framework provides a general a priori identification of emerging public health conditions rather than a specific illness (e.g., influenza) as is commonly done.

References

[1]
Twenty six percent of online adults discuss health information online; privacy cited as the biggest barrier to entry. http://www.businesswire.com/news/home/20121120005872/en
[2]
Twitter blogs: measuring tweets. http://blog.twitter.com/2010/02/measuring-tweets.html.
[3]
Twitter statistics. http://www.statisticbrain.com/twitter-statistics/
[4]
http://www.google.org/flutrends/us/#US
[5]
R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of 20th International Conference on Very Large Data Bases, VLDB, pages 487--499, 1994.
[6]
E. Aramaki, S. Maskawa, and M. Morita. Twitter catches the flu: Detecting influenza epidemics using twitter. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP, pages 1568--1576, 2011.
[7]
D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3: 993--1022, 2003.
[8]
S. T. Brown, J. H. Tai, R. R. Bailey, P. C. Cooley, W. D. Wheaton, M. A. Potter, R. E. Voorhees, M. Lejeune, J. J. Grefenstette, D. S. Burke, S. M. McGlone, B. Y. Lee. Would school closure for the 2009 H1N1 influenza epidemic have been worth the cost?: a computational simulation of Pennsylvania. BMC Public Health. 2011, May 20; 11(1): 353.
[9]
J. Chang, J. L. Boyd-Graber, S. Gerrish, C. Wang, and D. M. Blei. Reading tea leaves: How humans interpret topic models. In Proceedings of the 23rd Annual Conference on Neural Information Processing Systems, NIPS, pages 288--296, 2009.
[10]
W. Chou, Y. Hunt, E. Beckjord, R. Moser, and B. Hesse. Social media use in the United States: implications for health communication. Journal of medical Internet research, 11(4), 2009.
[11]
A. Cohen. Optimizing feature representation for automated systematic review work prioritization. In Proceedings of AMIA Annual Symposium, volume 2008, page 121, 2008.
[12]
A. Cohen, K. Ambert, and M. McDonagh. Cross-topic learning for work prioritization in systematic review creation and update. Journal of the American Medical Informatics Association, 16(5): 690--704, 2009.
[13]
C. Corley, A. Mikler, K. Singh, and D. Cook. Monitoring influenza trends through mining social media. In Proceedings of the International Conference on Bioinformatics Computational Biology, ICBCB, pages 340--346, 2009.
[14]
A. Culotta. Towards detecting influenza epidemics by analyzing twitter messages. In Proceedings of the 1st Workshop on Social Media Analytics, pages 115--122, 2010.
[15]
E. Diaz-Aviles, A. Stewart, E. Velasco, K. Denecke, and W. Nejdl. Towards personalized learning to rank for epidemic intelligence based on social media streams. In Proceedings of the 21st international conference companion on World Wide Web, WWW, pages 495--496, 2012.
[16]
J. M. Epstein, D. M. Goedecke, F. Yu, R. J. Morris, D. K. Wagener, et al. (2007) Controlling Pandemic Flu: The Value of International Air Travel Restrictions. PLoS ONE 2(5): e401.
[17]
J. Ginsberg, M. Mohebbi, R. Patel, L. Brammer, M. Smolinski, and L. Brilliant. Detecting influenza epidemics using search engine query data. Nature, 457(7232): 1012--1014, 2008.
[18]
S. Jamison-Powell, C. Linehan, L. Daley, A. Garbett, and S. Lawson. "i can't get no sleep": discussing #insomnia on twitter. In Proceedings of the ACM annual conference on Human Factors in Computing Systems, CHI, pages 1501--1510, 2012.
[19]
B. Jansen, M. Zhang, K. Sobel, and A. Chowdury. Twitter power: Tweets as electronic word of mouth. Journal of the American society for information science and technology, 60(11): 2169--2188, 2009
[20]
V. Lampos and N. Cristianini. Nowcasting events from the social web with statistical learning. ACM Transactions on Intelligent Systems and Technology, 3(4): 72, 2012.
[21]
H. Li, Y. Wang, D. Zhang, M. Zhang, and E. Chang. PFP: Parallel FP-growth for query recommendation. In Proceedings of the ACM Conference on Recommender Systems, pages 107--114, 2008
[22]
E. Mykhalovskiy, L. Weir, et al. The global public health intelligence network and early warning outbreak detection: a Canadian contribution to global public health. Canadian journal of public health, 97(1): 42, 2006.
[23]
A. Nakhasi, R. Passarella, S. Bell, M. Paul, M. Dredze, and P. Pronovost. Malpractice and malcontent: Analyzing medical complaints in twitter. In AAAI Fall Symposium Series, 2012.
[24]
B. O'Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith. From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the 4th International Conference on Weblogs and Social Media, ICWSM, 2010.
[25]
J. Parker and J. M. Epstein. A Distributed Platform for Global-Scale Agent-Based Models of Disease Transmission. ACM Trans. Model. Comput. Simul. 22, 1, Article 2 (December 2011), 25 pages.
[26]
M. Paul and M. Dredze. A model for mining public health topics from twitter. HEALTH, 11: 16--6, 2012.
[27]
M. J. Paul and R. Girju. A two-dimensional topic-spect model for discovering multi-faceted topics. In Proceedings of the 24th AAAI Conference on Artificial Intelligence, 2010.
[28]
T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web, WWW, pages 851--860, 2010.
[29]
B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, and M. Demirbas. Short text classification in twitter to improve information filtering. In Proceeding of the 33rd international ACM SIGIR conference on research and development in information retrieval, SIGIR, pages 841--842, 2010.
[30]
A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe. Predicting elections with twitter: What 140 characters reveal about political sentiment. In Proceedings of the 4th International Conference on Weblogs and Social Media, ICWSM, 2010.
[31]
I. Uysal and W. B. Croft. User oriented tweet ranking: a filtering approach to microblogs. In Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM, pages 2261--2264, 2011.
[32]
B. Wenerstrom, M. Kantardzic, E. Arabmakki, and M. Hindi. Multitweet summarization for flu outbreak detection. In AAAI Fall Symposium Series, 2012
[33]
R. W. White RW, N. P. Tatonetti, N. H. Shah, R. B. Altman, E. Horvitz E. Web-scale pharmacovigilance: listening to signals from the crowd. J Am Med Inform Assoc. 2013 May 1; 20(3): 404--8. Epub 2013 Mar 6.
[34]
A. Yates and N. Goharian, "ADRTrace: Detecting Expected and Unexpected Adverse Drug Reactions from User Reviews on Social Media Sites", In Proceedings of the 35th European Conference on Information Retrieval (ECIR 2013), 2013
[35]
Y. Zhu and N. Goharian, "To Follow or Not to Follow: A Feature Evaluation", Proceedings of the 22nd international conference on World Wide Web (WWW'13), 2013.

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  • (2022)World Health Organization’s Twitter Use Before and During Covid-19 Pandemic: Sentiment and Textual Analysis of TweetsDünya Sağlık Örgütü’nün Covid-19 Pandemisi Öncesi ve Sırasında Twitter Kullanımı: Tweetlerin Duygu ve Metin AnaliziIntermedia International E-journal10.56133/intermedia.11630329:17(235-254)Online publication date: 31-Dec-2022
  • (2021)Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derlemeÖmer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi10.28948/ngumuh.778948Online publication date: 6-Jan-2021
  • (2021)On Comparative Classification of Relevant Covid-19 Tweets2021 6th International Conference on Computer Science and Engineering (UBMK)10.1109/UBMK52708.2021.9558945(287-291)Online publication date: 15-Sep-2021
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cover image ACM Conferences
ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
August 2013
1558 pages
ISBN:9781450322409
DOI:10.1145/2492517
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 August 2013

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Author Tags

  1. Twitter
  2. Wikipedia
  3. health surveillance
  4. item-set mining

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ASONAM '13
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ASONAM '13: Advances in Social Networks Analysis and Mining 2013
August 25 - 28, 2013
Ontario, Niagara, Canada

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Cited By

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  • (2022)World Health Organization’s Twitter Use Before and During Covid-19 Pandemic: Sentiment and Textual Analysis of TweetsDünya Sağlık Örgütü’nün Covid-19 Pandemisi Öncesi ve Sırasında Twitter Kullanımı: Tweetlerin Duygu ve Metin AnaliziIntermedia International E-journal10.56133/intermedia.11630329:17(235-254)Online publication date: 31-Dec-2022
  • (2021)Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derlemeÖmer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi10.28948/ngumuh.778948Online publication date: 6-Jan-2021
  • (2021)On Comparative Classification of Relevant Covid-19 Tweets2021 6th International Conference on Computer Science and Engineering (UBMK)10.1109/UBMK52708.2021.9558945(287-291)Online publication date: 15-Sep-2021
  • (2020)COVID-19 vaccine hesitancy in Canada: a content analysis of Tweets using the Theoretical Domains Framework (Preprint)Journal of Medical Internet Research10.2196/26874Online publication date: 31-Dec-2020
  • (2020)Microblog topic identification using Linked Open DataPLOS ONE10.1371/journal.pone.023686315:8(e0236863)Online publication date: 11-Aug-2020
  • (2020)A graph-based approach for population health analysis using Geo-tagged tweetsMultimedia Tools and Applications10.1007/s11042-020-10034-0Online publication date: 26-Oct-2020
  • (2020)Topic Modeling on Twitter Data and Identifying Health-Related IssuesInformation Management and Machine Intelligence10.1007/978-981-15-4936-6_6(57-64)Online publication date: 17-Sep-2020
  • (2019)Crowdbreaks: Tracking Health Trends Using Public Social Media Data and CrowdsourcingFrontiers in Public Health10.3389/fpubh.2019.000817Online publication date: 12-Apr-2019
  • (2019)Internet-Based Sources of Health Information: A Systematic Literature Review (Preprint)Journal of Medical Internet Research10.2196/13680Online publication date: 24-Feb-2019
  • (2019)Analysis of Early Detection of Emerging Patterns from Social Media Networks: A Data Mining Techniques PerspectiveSoft Computing and Signal Processing10.1007/978-981-13-3600-3_2(15-25)Online publication date: 17-Jan-2019
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