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The reliability of tweets as a supplementary method of seasonal influenza surveillance.
J Med Internet Res. 2014 Nov 14; 16(11):e250.JM

Abstract

BACKGROUND

Existing influenza surveillance in the United States is focused on the collection of data from sentinel physicians and hospitals; however, the compilation and distribution of reports are usually delayed by up to 2 weeks. With the popularity of social media growing, the Internet is a source for syndromic surveillance due to the availability of large amounts of data. In this study, tweets, or posts of 140 characters or less, from the website Twitter were collected and analyzed for their potential as surveillance for seasonal influenza.

OBJECTIVE

There were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ILI) rates by city through filtering and a machine-learning classifier, (2) to observe correlations of tweets for emergency department ILI rates by city, and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in San Diego.

METHODS

Tweets containing the keyword "flu" were collected within a 17-mile radius from 11 US cities selected for population and availability of ILI data. At the end of the collection period, 159,802 tweets were used for correlation analyses with sentinel-provided ILI and emergency department ILI rates as reported by the corresponding city or county health department. Two separate methods were used to observe correlations between tweets and ILI rates: filtering the tweets by type (non-retweets, retweets, tweets with a URL, tweets without a URL), and the use of a machine-learning classifier that determined whether a tweet was "valid", or from a user who was likely ill with the flu.

RESULTS

Correlations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier.

CONCLUSIONS

Compared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data.

Authors+Show Affiliations

Graduate School of Public Health, San Diego State University, San Diego, CA, United States.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

25406040

Citation

Aslam, Anoshé A., et al. "The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance." Journal of Medical Internet Research, vol. 16, no. 11, 2014, pp. e250.
Aslam AA, Tsou MH, Spitzberg BH, et al. The reliability of tweets as a supplementary method of seasonal influenza surveillance. J Med Internet Res. 2014;16(11):e250.
Aslam, A. A., Tsou, M. H., Spitzberg, B. H., An, L., Gawron, J. M., Gupta, D. K., Peddecord, K. M., Nagel, A. C., Allen, C., Yang, J. A., & Lindsay, S. (2014). The reliability of tweets as a supplementary method of seasonal influenza surveillance. Journal of Medical Internet Research, 16(11), e250. https://doi.org/10.2196/jmir.3532
Aslam AA, et al. The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance. J Med Internet Res. 2014 Nov 14;16(11):e250. PubMed PMID: 25406040.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - The reliability of tweets as a supplementary method of seasonal influenza surveillance. AU - Aslam,Anoshé A, AU - Tsou,Ming-Hsiang, AU - Spitzberg,Brian H, AU - An,Li, AU - Gawron,J Mark, AU - Gupta,Dipak K, AU - Peddecord,K Michael, AU - Nagel,Anna C, AU - Allen,Christopher, AU - Yang,Jiue-An, AU - Lindsay,Suzanne, Y1 - 2014/11/14/ PY - 2014/05/13/received PY - 2014/09/22/accepted PY - 2014/08/21/revised PY - 2014/11/19/entrez PY - 2014/11/19/pubmed PY - 2015/4/10/medline KW - Internet KW - Twitter KW - influenza KW - infodemiology KW - infoveillance KW - syndromic surveillance KW - tweets SP - e250 EP - e250 JF - Journal of medical Internet research JO - J. Med. Internet Res. VL - 16 IS - 11 N2 - BACKGROUND: Existing influenza surveillance in the United States is focused on the collection of data from sentinel physicians and hospitals; however, the compilation and distribution of reports are usually delayed by up to 2 weeks. With the popularity of social media growing, the Internet is a source for syndromic surveillance due to the availability of large amounts of data. In this study, tweets, or posts of 140 characters or less, from the website Twitter were collected and analyzed for their potential as surveillance for seasonal influenza. OBJECTIVE: There were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ILI) rates by city through filtering and a machine-learning classifier, (2) to observe correlations of tweets for emergency department ILI rates by city, and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in San Diego. METHODS: Tweets containing the keyword "flu" were collected within a 17-mile radius from 11 US cities selected for population and availability of ILI data. At the end of the collection period, 159,802 tweets were used for correlation analyses with sentinel-provided ILI and emergency department ILI rates as reported by the corresponding city or county health department. Two separate methods were used to observe correlations between tweets and ILI rates: filtering the tweets by type (non-retweets, retweets, tweets with a URL, tweets without a URL), and the use of a machine-learning classifier that determined whether a tweet was "valid", or from a user who was likely ill with the flu. RESULTS: Correlations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier. CONCLUSIONS: Compared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data. SN - 1438-8871 UR - https://www.unboundmedicine.com/medline/citation/25406040/The_reliability_of_tweets_as_a_supplementary_method_of_seasonal_influenza_surveillance_ L2 - https://www.jmir.org/2014/11/e250/ DB - PRIME DP - Unbound Medicine ER -