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Towards personalized learning to rank for epidemic intelligence based on social media streams

Published: 16 April 2012 Publication History

Abstract

In the presence of sudden outbreaks, how can social media streams be used to strengthen surveillance capabilities? In May 2011, Germany reported one of the largest described outbreaks of Enterohemorrhagic Escherichia coli (EHEC). By end of June, 47 persons had died. After the detection of the outbreak, authorities investigating the cause and the impact in the population were interested in the analysis of micro-blog data related to the event. Since Thousands of tweets related to this outbreak were produced every day, this task was overwhelming for experts participating in the investigation. In this work, we propose a Personalized Tweet Ranking algorithm for Epidemic Intelligence (PTR4EI), that provides users a personalized, short list of tweets based on the user's context. PTR4EI is based on a learning to rank framework and exploits as features, complementary context information extracted from the social hash-tagging behavior in Twitter. Our experimental evaluation on a dataset, collected in real-time during the EHEC outbreak, shows the superior ranking performance of PTR4EI. We believe our work can serve as a building block for an open early warning system based on Twitter, helping to realize the vision of Epidemic Intelligence for the Crowd, by the Crowd.

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E. Diaz-Aviles, A. Stewart, E. Velasco, K. Denecke, and W. Nejdl. Epidemic Intelligence for the Crowd, by the Crowd (full version). http://arxiv.org/, 2012.
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T.-Y. Liu. Learning to Rank for Information Retrieval. Found. Trends Inf. Retr., 3:225--331, March 2009.
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M. J. Paul and M. Dredze. You Are What You Tweet: Analyzing Twitter for Public Health. In ICWSM'11, 2011.
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Semiocast. Countries on Twitter. http://goo.gl/RfxZw, 2012.
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Cited By

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  • (2019)Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective AnalysisJournal of Medical Internet Research10.2196/1045021:2(e10450)Online publication date: 20-Feb-2019
  • (2018)Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining StudyJMIR Public Health and Surveillance10.2196/publichealth.86274:3(e65)Online publication date: 25-Sep-2018
  • (2018)Twitter Health Surveillance (THS) System2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622504(1647-1654)Online publication date: Dec-2018
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Published In

cover image ACM Other conferences
WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web
April 2012
1250 pages
ISBN:9781450312301
DOI:10.1145/2187980

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  • Univ. de Lyon: Universite de Lyon

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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Author Tags

  1. learning to rank
  2. recommender systems
  3. twitter

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  • Poster

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WWW 2012
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  • Univ. de Lyon
WWW 2012: 21st World Wide Web Conference 2012
April 16 - 20, 2012
Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2019)Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective AnalysisJournal of Medical Internet Research10.2196/1045021:2(e10450)Online publication date: 20-Feb-2019
  • (2018)Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining StudyJMIR Public Health and Surveillance10.2196/publichealth.86274:3(e65)Online publication date: 25-Sep-2018
  • (2018)Twitter Health Surveillance (THS) System2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622504(1647-1654)Online publication date: Dec-2018
  • (2017)Experiences with the Twitter Health Surveillance (THS) System2017 IEEE International Congress on Big Data (BigData Congress)10.1109/BigDataCongress.2017.55(376-383)Online publication date: Jun-2017
  • (2017)Towards Exploiting Social Networks for Detecting Epidemic OutbreaksGlobal Journal of Flexible Systems Management10.1007/s40171-016-0148-y18:1(61-71)Online publication date: 11-Jan-2017
  • (2016)After the boom no one tweetsProceedings of the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory10.1145/3007818.3007822(17-25)Online publication date: 17-Oct-2016
  • (2016)Challenges in Detecting Epidemic Outbreaks from Social Networks2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA)10.1109/WAINA.2016.111(69-74)Online publication date: Mar-2016
  • (2015)Health-related hypothesis generation using social media dataSocial Network Analysis and Mining10.1007/s13278-014-0239-85:1Online publication date: 5-Mar-2015
  • (2013)A framework for detecting public health trends with TwitterProceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/2492517.2492544(556-563)Online publication date: 25-Aug-2013
  • (2013)Living analytics methods for the web observatoryProceedings of the 22nd International Conference on World Wide Web10.1145/2487788.2488169(1321-1324)Online publication date: 13-May-2013
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