Automatic online news monitoring and classification for syndromic surveillance

Decision Support Systems

Volumn 47, Issue 4, 2009, Pages 508-517

  Zhang, Yulei ^a   Dang, Yan ^a   Chen, Hsinchun ^a   Thurmond, Mark ^b   Larson, Cathy ^a  

^a UNIVERSITY OF ARIZONA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Feature selection; News classification; News monitoring; Syndromic surveillance

Indexed keywords

BAG OF WORDS; CLASSIFICATION PERFORMANCE; CLASSIFICATION SYSTEM; COMBINED FEATURES; DEVASTATING EFFECTS; FEATURE SELECTION; FEATURE SUBSET; GLOBAL AWARENESS; INFECTIOUS DISEASE; MACHINE LEARNING ALGORITHMS; NAMED ENTITIES; NOUN PHRASE; ONLINE NEWS; PUBLIC HEALTH; SYNDROMIC SURVEILLANCE;

FEATURE EXTRACTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; SET THEORY;

MONITORING;

EID: 70350564445     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2009.04.016     Document Type: Article

References (49)

1. Abbasi A., and Chen H. Identification and comparison of extremist-group web forum messages using authorship analysis. IEEE Intelligent Systems 20 5 (2005) 67-75
2. Aberdeen J., Burger J., Day D., Hirschman L., Palmer D., Robinson P., and Vilain M. MITRE: description of the Alembic system as used in MET. Proceedings of the TIPSTER 24-Month Workshop (1996)
3. Aberdeen J., Burger J., Day D., Hirschman L., Robinson P., and Vilain M. MITRE: description of the Alembic system as used for MUC-6. Proceedings of the Sixth Message Understanding Conference (MUC-6) (1995)
4. Bessell P.R., Keeling M.J., Tildesley M.J., and Woolhouse M.E.J. T8.6: Future risks of foot and mouth disease spread in the UK, infectious diseases: preparing for the future (2006), Office of Sscience and Innovation
5. Bravata D.M., McDonald K.M., Smith W.M., Rydzak C., Szeto H., Buckeridge D.L., Haberland C., and Owens D.K. Systematic review: surveillance systems for early detection of bioterrorism-related diseases. Annals of Internal Medicine 140 11 (2004) 910-922
6. Brownstein J., and Freifeld C. HealthMap: the development of automated real-time internet surveillance for epidemic intelligence. Eurosurveillance 12 48 (2007)
7. Brownstein J.S., Freifeld C.C., Reis B.Y., and Mandl K.D. Evaluation of online media reports for global infectious disease intelligence. Advances in Disease Surveillance 4 3 (2007)
8. Chakrabarti S., Berg M.V.D., and Dom B. Focused crawling: a new approach to topic-specific web resource discovery. Proceedings of the 8th International World Wide Web Conference (1999)
9. Chang W., Zeng D., and Chen H. A stack-based prospective spatio-temporal data analysis approach. Decision Support Systems (2008) 10.1016/j.dss.2007.12.008
10. Chau M., and Chen H. Comparison of three vertical search spiders. Computer 36 5 (2003) 56-62
11. Chen H., Chung Y., Ramsey M., and Yang C. A smart itsy bitsy spider for the web. Journal of the American Society for Information Science 49 7 (1998) 604-618
12. Damianos L., Wohlever S., Kozierok R., and Ponte J. MiTAP: a case study of integrated knowledge discovery tools. Proceedings of the 36th Hawaii International Conference on System Sciences (HICSS'03) (2003)
13. Dash M., and Liu H. Feature selection for classification. Intell. Data Anal. 1 3 (1997) 131-156
14. Freifeld C.C., Mandl K.D., Reis B.Y., and Brownstein J.S. HealthMap: global infectious disease monitoring through automated classification and visualization of internet media reports. Journal of the American Medical Informatics Association 15 (2008) 150-157
15. Hall M. Correlation-based feature selection for discrete and numeric class machine learning. Proceedings of the 17th International Conference on Machine Learning (ICML 2000) (2000)
16. Hall M.A. Correlation-based feature selection for machine learning (1998), Deptartment of Computer Science, Univiversity of Waikato
17. Hall M.A., and Holmes G. Benchmarking attribute selection techniques for data mining. IEEE Transactions on Knowledge and Data Engineering 15 6 (2003) 1437-1447
18. Heymann D.L., and Rodier G.R. WHO operational support team to the global outbreak alert and response network. Hot spots in a wired world: WHO surveillance of emerging and re-emerging infectious diseases. Lancet Infectious Diseases 1 (2001) 345-353
19. Hu P.J.-H., Zeng D., Chen H., Larson C., Chang W., Tseng C., and Ma J. System for infectious disease information sharing and analysis: design and evaluation. IEEE Transactions on information technology in biomedicine 11 4 (2007) 483-492
20. Joachims T. Text categorization with support vector machines: learning with many relevant features. proceedings of 10th European Conference on Machine Learning (ECML 98) (1998) 137-142
21. Johnson J., Tsioutsiouliklis K., and Giles C.L. Evolving strategies for focused web crawling. Proceedings of the 20th International Conference on Machine Learning (ICML 2003), Washington DC (2003)
22. Li J., Su H., Chen H., and Futscher B.W. Optimal search-based gene subset selection for gene array cancer classification. IEEE Transactions on information technology in biomedicine 11 4 (2007)
23. Liao C., Alpha S., and Dixon P. Feature preparation in text categorization. In: Hegland e.S.J.S.a.G.J.W.a.M. (Ed). ADM03 workshop (Australian Data Mining Workshop), Canberra, Australia (2003) 143-162
24. López F.G.A., Torres M.G.A., Batista B.M., Pérez J.A.M., and Moreno-Vega J.M. Solving feature subset selection problem by a parallel scatter search. European Journal of Operational Research 169 2 (2006) 477-489
25. Ma H., Rolka H., Mandl K., Buckeridge D., Fleischauer A., and Pavlin J. Implementation of laboratory order data in biosense early event detection and situation awareness system. MMWR (CDC) 54 Suppl (2005) 27-30
26. Madoff L. Cooperation between animal and human health sectors is key to the detection, surveillance, and control of emerging disease. Eurosurveillance 11 51 (2006)
27. Mani I., and Bloedorn E. Summarizing similarities and differences among related documents. Information Retrieval 1 1 (1999) 35-67
28. McDonald D.M., Chen H., and Schumaker R.P. Transforming open-source documents to terror networks: the Arizona TerrorNet. American Association for Artificial Intelligence Conference Spring Symposia, Stanford, CA (2005)
29. Meiri R., and Zahavi J. Using simulated annealing to optimize the feature selection problem in marketing applications. European Journal of Operational Research 171 3 (2006) 842-858
30. Menczer F., and Belew R.K. Adaptive retrieval agents: internalizing local context and scaling up to the web. Machine Learning 39 (2000) 203-242
31. J. Miller, Website for the germ-obsessed: to its devotees, ProMED, which tracks disease outbreaks worldwide, is a must-read, Los Angeles Times, 2007.
32. Moldovan D., Pasca M., Harabagiu S., and Surdeanu M. Performance issues and error analysis in an open-domain question answering system. ACM Transactions on Information Systems 21 2 (2003) 133-154
33. Pang B., Lee L., and Vaithyanathain S. Thumbs up? Sentiment classification using machine learning techniques. Proceedings of the Conference on Empirical Methods in Natural Language Processing (2002) 79-86
34. Pant G., and Srinivasan P. Learning to crawl: comparing classification schemes. ACM Transactions on Information Systems 23 4 (2005) 430-462
35. Pinner R.W., Rebmann C.A., Schuchat A., and Hughes J.M. Disease surveillance and the academic, clinical, and public health communities. Emerging Infectious Diseases 9 7 (2003) 781-787
36. Rennie J., and Mccallum A.K. Using reinforcement learning to spider the web efficiently. Proceedings of the 16th International Conference on Machine Learning, Morgan Kaufmann, San Francisco,CA (1999) 335-343
37. Schumaker R., and Chen H. Evaluating a news-aware quantitative trader: the effect of momemtum and contrarian stock selection strategies. Journal of the American Society for Information Science and technology 59 2 (2008) 247-255
38. Schumaker R., and Chen H. Textual analysis of stock market prediction using financial news articles. Americas Conference on Information Systems (AMCIS 2006), Acapulco, Mexico (2006)
39. Scott S., and Matwin S. Feature engineering for text classification. Proceedings of ICML-99, 16th International Conference on Machine Learning (1999) 379-388
40. Sebastiani F. Machine learning in automated text categorization. ACM Computing Surveys 34 1 (2002) 1-47
41. Sekine S., and Nobata C. Definition, dictionaries and tagger for extended named entity hierarchy. Forth International Conference on Language Resources and Evaluation, Lisbon, Portugal (2004)
42. Tolle K.M., and Chen H. Comparing noun phrasing techniques for use with medical digital library tools. Journal of the American Society for Information Science 51 4 (2000) 352-370
43. Wei C.-P., and Lee Y.-H. Event detection from online news documents for supporting environmental scanning. Decision Support Systems 36 4 (2004) 385-401
44. Wilson J.M. Argus: a global detection and tracking system for biological events. Advances in Disease Surveillance 4 21 (2007)
45. Wilson J.M. Indications and warnings to dectect and track biological events. Presented at the Sixth Annual International Society for Disease Surveillance Conference Indianapolis, IN (2007)
46. Wilson J.M., Polyak M.G., Blake J.W., and Collmann J. A heuristic indication and warning staging model for detection and assessment of biological events. Journal of the American Medical Informatics Association 15 2 (2008) 158-171
47. Witten I.H., and Frank E. Data mining, practical machine learning tools and techniques. 2nd ed. (2005), Morgan Kaufmann, San Francisco, California
48. Yan P., Chen H., and Zeng D. Syndromic surveillance systems: public health and biodefense. Annual Review of Information Sciences and Technology 42 (2008) 425-495
49. Zheng R., Li J., Huang Z., and Chen H. A framework for authorship analysis of online messages: writing-style features and techniques. Journal of the American Society for Information Science and technology 57 3 (2006) 378-393