Syndromic surveillance of Flu on Twitter using weakly supervised temporal topic models

Data Mining and Knowledge Discovery

Volumn 30, Issue 3, 2016, Pages 681-710

  Chen, Liangzhe ^a   Tozammel Hossain, K S M ^a   Butler, Patrick ^a   Ramakrishnan, Naren ^a   Prakash, B Aditya ^a  

^a Virginia Polytechnic Institute and State University   (United States)

Author keywords

Hidden Markov model; Social media; Syndromic surveillance; Topic model

Indexed keywords

ARTIFICIAL INTELLIGENCE; FILE EDITORS; FORECASTING; HIDDEN MARKOV MODELS; LEARNING SYSTEMS; MARKOV PROCESSES; MONITORING;

DISEASE SURVEILLANCE; EPIDEMIOLOGICAL MODELS; ESTIMATION OF TRENDS; MACHINE LEARNING TECHNIQUES; OPTIMAL PREDICTIONS; SOCIAL MEDIA; SYNDROMIC SURVEILLANCE; TOPIC MODELING;

SOCIAL NETWORKING (ONLINE);

EID: 84941008120     PISSN: 13845810     EISSN: 1573756X     Source Type: Journal    
DOI: 10.1007/s10618-015-0434-x     Document Type: Article

References (42)

1. Achrekar H, Gandhe A, Lazarus R, Yu S-H, and Liu B (2011) Predicting flu trends using twitter data. In: 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS). pp 702–707
2. Anderson RM, May RM (1991) Infectious diseases of humans. Oxford University Press, Oxford
3. Andrews M, Vigliocco G (2010) The hidden markov topic model: a probabilistic model of semantic representation. Top Cogn Sci 2(1):101–113
4. Aramaki E, Maskawa S, Morita M (2011) Twitter catches the flu: detecting influenza epidemics using twitter. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP ’11). pp 1568–1576
5. Beretta E, Takeuchi Y (1995) Global stability of an SIR epidemic model with time delays. J Math Biol 33(3):250–260
6. Blasiak S, Rangwala H (2011) A hidden Markov model variant for sequence classification. In: The 21nd international joint conference on artificial intelligence. pp 1192–1197
7. Blei D, Carin L, Dunson D (2010) Probabilistic topic models. Signal Process Mag IEEE 27(6):55–65
8. Blei D, Lafferty J (2006) Dynamic topic models. In: The 23rd international conference on machine learning. pp 113–120
9. Blei D, Ng A, Jordan M (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022
10. Brennan SP, Sadilek A, Kautz HA (2013) Towards understanding global spread of disease from everyday interpersonal interactions. In: Proceedings of the 23rd international joint conference on artificial intelligence. AAAI Press, pp 2783–2789
11. Butler D (2013) When Google got Flu wrong. Nature 494(7436):155–156
12. Chakraborty P, Khadivi P, Lewis B, Mahendiran A, Chen J, Butler P, Nsoesie E, Mekaru S, Brownstein J, Marathe M, Ramakrishnan N (2014) Forecasting a moving target: ensemble models for ili case count predictions. In: 2014 SIAM international conference on data mining (SDM ’14)
13. Chen L, Hossain KSMT, Butler P, Ramakrishnan N, Prakash BA (2014) Flu gone viral: Syndromic surveillance of flu on twitter using temporal topic models. In: Proceedings of the fifth IEEE international conference on data mining (ICDM ’14)
14. Christakis NA, Fowler JH (2010) Social network sensors for early detection of contagious outbreaks. PLoS One 5(9):e12948
15. Crane R, Sornette D (2008) Robust dynamic classes revealed by measuring the response function of a social system. Proc Natl Acad Sci 105(41):15649–15653
16. Culotta A (2010) Towards detecting influenza epidemics by analyzing twitter messages. In: Proceedings of the first workshop on social media analytics. ACM, pp 115–122
17. Ginsberg J, Mohebbi M, Patel R, Brammer L, Smolinski M, Brilliant L (2008) Detecting influenza epidemics using search engine query data. Nature 457(7232):1012–1014
18. Glance N, Hurst M, Tomokiyo T (2004) Blogpulse: automated trend discovery for weblogs. WWW 2004 workshop on the weblogging ecosystem: aggregation, analysis and dynamics
19. Gruber A, Weiss Y, Rosen-Zvi M (2007) Hidden topic markov models. In: International conference on artificial intelligence and statistics. pp 163–170
20. Hethcote HW (2000) The mathematics of infectious diseases. Soc Ind Appl Math SIAM Rev 42(4):599–653
21. Hong L, Yin D, Guo J, Davison B (2011) Tracking trends: incorporating term volume into temporal topic models. In: the 17th ACM SIGKDD international conference on knowledge discovery and data mining. pp 484–492
22. Jacquez J, Simon C (1993) The stochastic SI model with recruitment and deaths I. Comparison with the closed SIS model. Math Biosci 117(1):77–125
23. Lamb A, Paul MJ, Dredze M (2013) Separating fact from fear: tracking flu infections on twitter. In: North American chapter of the association for computational linguistics (NAACL). pp 789–795
24. Lampos V, Cristianini N (2012) Nowcasting events from the social web with statistical learning. ACM Trans Intell Syst Technol 3(4):72
25. Lampos V, De Bie T, Cristianini N (2010) Flu detector: tracking epidemics on twitter. In: Proceedings of the 2010 European conference on machine learning and knowledge discovery in databases: Part III (ECML PKDD’10). pp 599–602
26. Lazer DM, Kennedy R, King G, Vespignani A (2014) The parable of google flu: traps in big data analysis. Science 343(6176):1203–1205
27. Lee K, Agrawal A, Choudhary A (2013) Real-time disease surveillance using twitter data: demonstration on flu and cancer. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining (KDD). ACM, pp 1474–1477
28. Li J, Cardie C (2013) Early stage influenza detection from twitter. arXiv:1309.7340
29. Li M, Muldowney J (1995) Global stability for the seir model in epidemiology. Math Biosci 125(2):155–164
30. Matsubara Y, Sakurai Y, Prakash BA, Li L, Faloutsos C (2012) Rise and fall patterns of information diffusion: model and implications. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ’12). pp 6–14
31. PAHO (2012). Epidemic disease database, pan american health organization. http://www.ais.paho.org/phip/viz/ed_flu.asp
32. Paul M, Dredze M (2011) You are what you tweet: analyzing twitter for public health. In: Fifth international AAAI conference on weblogs and social media (ICWSM 2011). pp 265–272
33. Paul M, Girju R (2010) A two-dimensional topic-aspect model for discovering multi-faceted topics. Urbana 51:61801
34. Romero DM, Meeder B, Kleinberg J (2011) Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th international conference on world wide web (WWW ’11). ACM, New York. pp 695–704
35. Sadilek A, Kautz H, Silenzio V (2012) Predicting disease transmission from geo-tagged micro-blog data. In: AAAI conference on artificial intelligence
36. Spasojevic N, Yan J, Rao A, Bhattacharyya P (2014) Lasta: large scale topic assignment on multiple social networks. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ’14). ACM, New York. pp 1809–1818
37. Steyvers M, Smyth P, Rosen-Zvi M, Griffiths T (2004) Probabilistic author-topic models for information discovery. In: The 10th ACM SIGKDD international conference on knowledge discovery and data mining. pp 306–315
38. Wang X, McCallum A (2006) Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ’06). pp 424–433
39. Yang J, Leskovec J (2011) Patterns of temporal variation in online media. In: Proceedings of the fourth ACM international conference on web search and data mining. ACM. pp 177–186
40. Yang J, McAuley J, Leskovec J, LePendu P, Shah N (2014a) Finding progression stages in time-evolving event sequences. In: Proceedings of the 23rd international conference on world wide web (WWW ’14). pp 783–794
41. Yang S-H, Kolcz A, Schlaikjer A, Gupta P (2014b) Large-scale high-precision topic modeling on twitter. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ’14). ACM, New York, pp 1907–1916
42. Zhao S, Zhong L, Wickramasuriya J, Vasudevan V (2011) Human as real-time sensors of social and physical events, A case study of twitter and sports games. arXiv:1106.4300