The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Tracking the flu pandemic by monitoring the social web
Abstract
Tracking the spread of an epidemic disease like seasonal or pandemic influenza is an important task that can reduce its impact and help authorities plan their response. In particular, early detection and geolocation of an outbreak are important aspects of this monitoring activity. Various methods are routinely employed for this monitoring, such as counting the consultation rates of general practitioners. We report on a monitoring tool to measure the prevalence of disease in a population by analysing the contents of social networking tools, such as Twitter. Our method is based on the analysis of hundreds of thousands of tweets per day, searching for symptom-related statements, and turning statistical information into a flu-score. We have tested it in the United Kingdom for 24 weeks during the H1N1 flu pandemic. We compare our flu-score with data from the Health Protection Agency, obtaining on average a statistically significant linear correlation which is greater than 95%. This method uses completely independent data to that commonly used for these purposes, and can be used at close time intervals, hence providing inexpensive and timely information about the state of an epidemic.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) · The Annals of Statistics · doi:10.1214/009053604000000067 · Cited by 9,357
An algorithm for suffix stripping
Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
Biosensors for detection of pathogenic bacteria
Ivnitski, D., Abdel-Hamid, I., Atanasov, P., & Wilkins, E. (1999) · Biosensors and Bioelectronics · doi:10.1016/s0956-5663(99)00039-1 · Cited by 931
Using Internet Searches for Influenza Surveillance
Polgreen, P. M., Chen, Y., Pennock, D. M., & Nelson, F. D. (2008) · Clinical Infectious Diseases · doi:10.1086/593098 · Cited by 566
Neuzil, K. M., Hohlbein, C., & Zhu, Y. (2002) · Archives of Pediatrics & Adolescent Medicine · doi:10.1001/archpedi.156.10.986 · Cited by 345
Web Queries as a Source for Syndromic Surveillance
Hulth, A., Rydevik, G., & Linde, A. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004378 · Cited by 225
Lessons from 40 years' surveillance of influenza in England and Wales
Fleming, D. M., & Elliot, A. J. (2007) · Epidemiology and Infection · doi:10.1017/s0950268807009910 · Cited by 109
Monitoring Influenza Trends through Mining Social Media.
Corley, C. D., Mikler, A. R., Singh, K. P., & Cook, D. J. (2009) · Cited by 57
Cited by
A topology of Twitter research: disciplines, methods, and ethics
Zimmer, M., & Proferes, N. J. (2014) · Aslib Journal of Information Management · doi:10.1108/ajim-09-2013-0083 · Cited by 257
Gaining insights from social media language: Methodologies and challenges.
Kern, M. L., Park, G., Eichstaedt, J. C., Schwartz, H. A., Sap, M., Smith, L. K., & Ungar, L. H. (2016) · Psychological Methods · doi:10.1037/met0000091 · Cited by 221
Gayo-Avello, D. (2012) · arXiv · doi:10.48550/arxiv.1204.6441 · Cited by 179
Using online social networks to track a pandemic: a systematic review
Al-garadi, M. A., Khan, M. S., Varathan, K. D., Mujtaba, G., & Al-Kabsi, A. M. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.05.005 · Cited by 166
Tracking COVID-19 using online search
Lampos, V., Majumder, M. S., Yom-Tov, E., Edelstein, M., Moura, S., Hamada, Y., Rangaka, M. X., McKendry, R. A., & Cox, I. J. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00384-w · Cited by 164
Self-citation
Metadata
- Authors
- Lampos, V., & Cristianini, N.
- Venue
- 2010 2nd International Workshop on Cognitive Information Processing
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 411-416
- OpenAlex
- W2141701578
- Cited by
- 304
- References
- 12
- FWCI
- 157.61
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Tracking the flu pandemic by monitoring the social web. https://app.cochat.ai/papers/10.1109/cip.2010.5604088
- The paper
- Lampos, V., & Cristianini, N. (2010). Tracking the flu pandemic by monitoring the social web. 2010 2nd International Workshop on Cognitive Information Processing. https://doi.org/10.1109/cip.2010.5604088
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.