The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Digital disease detection - harnessing the Web for public health surveillance
Open accessAbstract
The Internet has become a critical medium for clinicians, public health practitioners, and laypeople seeking health information. Data about diseases and outbreaks are disseminated not only through online announcements by government agencies but also through informal channels, ranging from press reports to blogs to chat rooms to analyses of Web searches (see Digital Resources for Disease Detection). Collectively, these sources provide a view of global health that is fundamentally different from that yielded by the disease reporting of the traditional public health infrastructure.1 Digital Resources for Disease Detection.Sample Web-based data sourcesProMED-mail, www.promedmail.orgGlobal Public Health Intelligence Network (GPHIN), . . .
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
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
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 567
Infodemiology: tracking flu-related searches on the web for syndromic surveillance.
Eysenbach, G. (2006) · PubMed · Cited by 414
Brownstein, J. S., Freifeld, C. C., Reis, B. Y., & Mandl, K. D. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050151 · Cited by 413
ProMED-mail: An Early Warning System for Emerging Diseases
Yu, V. L., & Madoff, L. C. (2004) · Clinical Infectious Diseases · doi:10.1086/422003 · Cited by 269
Flagged first, then by citations
Cited by
Sorted by citations
Items 501–600 of 816
Patterson, M. T., & Grossman, R. L. (2017) · Big Data · doi:10.1089/big.2017.0028 · Cited by 3
An Opportunistic Network Approach Towards Disease Spreading
Joneydi, S., Khansari, M., & Kaveh, A. (2017) · Studies in computational intelligence · doi:10.1007/978-3-319-72150-7_26 · Cited by 4
Onega, T., Kamra, D., Alford‐Teaster, J., & Hassanpour, S. (2018) · JCO Clinical Cancer Informatics · doi:10.1200/cci.17.00150 · Cited by 4
Earth observation for public health: Biodiversity change and emerging disease surveillance
Malloy, S. S., Horack, J. M., Lee, J., & Newton, E. K. (2018) · Acta Astronautica · doi:10.1016/j.actaastro.2018.10.042 · Cited by 4
Mukhopadhyay, S., Samaddar, S., Solis, A. O., & Roy, A. (2018) · Decision Sciences · doi:10.1111/deci.12348 · Cited by 5
Engaging Women in Pelvic Floor Disorders Research Using the Internet
Pandya, L. K., Hundley, A. F., Hudson, C. O., Nekkanti, S., Smith, P. E., & Lynch, C. D.-J. (2019) · Urogynecology · doi:10.1097/spv.0000000000000785 · Cited by 4
Sentiment Analysis on Tweets for a Disease and Treatment Combination
Meena, R., Bai, V. T., & Omana, J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-37218-7_134 · Cited by 4
Transformative Power of Language Policies in Higher Education
Docrat, Z., & Kaschula, R. H. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671088.006 · Cited by 5
The Impact of COVID-19 on Psychiatric Health in the Korean Population
Wang, S., Kim, S., Choi, W.-S., Lim, H. K., Woo, Y. S., Pae, C., & Bahk, W. (2023) · Clinical Psychopharmacology and Neuroscience · doi:10.9758/cpn.23.1083 · Cited by 4
Multiple-Perspective Data-Driven Analysis of Online Health Communities
Alnashwan, R. O., O'Riordan, A. P., & Sorensen, H. (2023) · Healthcare · doi:10.3390/healthcare11202723 · Cited by 4
Social and Consumer Informatics
Greaves, F., & Rozenblum, R. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809523-2.00018-2 · Cited by 4
Corpora as Agency in the Intellectualisation of African Languages
Khumalo, L. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671088.014 · Cited by 4
Harvesting social media with artificial intelligence for medical threats mapping and analytics
David, W., King‐Okoye, M., Capone, A., Sensidoni, G., & Piovan, S. (2021) · Proceedings of the ICA · doi:10.5194/ica-proc-4-24-2021 · Cited by 4
Trends of Public Interest in Sleep Disorders: Looking by Internet Searching Volume
Ji, K., & Kang, M. (2017) · Sleep Medicine Research · doi:10.17241/smr.2017.00101 · Cited by 4
Research Ethics in the European Influenzanet Consortium: Scoping Review
Geneviève, L. D., Wangmo, T., Dietrich, D., Woolley-Meza, O., Flahault, A., & Elger, B. S. (2018) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.9616 · Cited by 5
From Postcolonial African Language Lexicography to Globally Competitive e-Lexicography in Africa
Prinsloo, D. J., & Zondi, N. B. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671088.015 · Cited by 4
Suggs, L. S., Rangelov, N., Schmeil, A., Occa, A., Bardus, M., Radchuk, O., & Dianat, S. (2014) · Wiley eBooks · doi:10.1002/9781118767771.wbiedcs080 · Cited by 4
Can Big Media Data Revolutionarize Gun Violence Prevention?Preprint
Ayers, J. W., Althouse, B. M., Leas, E. C., Alcorn, T., & Dredze, M. H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01148 · Cited by 5
The Premise and Promise of Big Data for Tracking Population Health: Big Deal or Big Disappointment?
Mansoor, E., & Al-Kindi, S. G. (2017) · Digestive Diseases and Sciences · doi:10.1007/s10620-017-4458-5 · Cited by 4
Covid19 Surveillance in Peru on April using Text MiningPreprint
Saire, J. E. C., & Oblitas, J. (2020) · medRxiv · doi:10.1101/2020.05.24.20112193 · Cited by 4
Nowcasting Credit Demand in Turkey with Google Trends DataPreprint
Uğurlu, E. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3476406 · Cited by 4
Incorporating connectivity among Internet search data for enhanced influenza-like illness tracking
Ning, S., Hussain, A., & Wang, Q. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305579 · Cited by 3
Özdenk, G. D., & Yeğin, Z. (2025) · Personalized Medicine · doi:10.1080/17410541.2025.2494501 · Cited by 4
Rosenberg, A. M., Taylor, B., Rasquinha, R. D., Khan, H. A., Quinn, M., Marcaccio, S. E., & Lemme, N. J. (2026) · Sports Health A Multidisciplinary Approach · doi:10.1177/19417381251411343 · Cited by 3
David, W., Garmendia-Doval, B., & King‐Okoye, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-31268-7_9 · Cited by 3
Sarasmita, M. A., Sudarma, I. W., & Susanty, S. (2023) · Indian Journal of Tuberculosis · doi:10.1016/j.ijtb.2023.05.009 · Cited by 2
Awareness campaigns for cochlear implants: Are we making an impact?
Kang, J. M., Bentan, M. A., & Coelho, D. H. (2024) · Cochlear Implants International · doi:10.1080/14670100.2024.2334550 · Cited by 3
Navigating Novel Viral Challenges: Understanding, Tracking, and Mitigating Emerging Threats
Branda, F., & Ciccozzi, M. (2024) · Microorganisms · doi:10.3390/microorganisms12040807 · Cited by 3
Ignaccolo, C., Wibisono, K. C., Sutto, M. P., & Plunz, R. A. (2024) · Journal of Urban Technology · doi:10.1080/10630732.2024.2364569 · Cited by 3
Yadav, A., & Singh, V. (2024) · Advances in experimental medicine and biology · doi:10.1007/5584_2024_836 · Cited by 3
Public Health Informatics: Increasing Use and Access
Strum, R. A., Fruhling, A., & Schumaker, A. M. (2012) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2012.492 · Cited by 3
Geospatial Applications in Disease Surveillance
Blatt, A. J. (2013) · International Journal of Applied Geospatial Research · doi:10.4018/jagr.2013040101 · Cited by 3
Manashe, L., Kabaso, B., Ralarala, M. K., & Ivala, E. N. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671088.009 · Cited by 3
Is Google Trends a reliable way to determine digital dental epidemiology?
Üner, D. D., & İzol, B. S. (2021) · International Dental Research · doi:10.5577/intdentres.2021.vol11.suppl1.7 · Cited by 3
Melms, L., Falk, E., Schieffer, B., Jerrentrup, A., Wagner, U. A. G., Matrood, S., Schaefer, J. R., Müller, T., & Hirsch, M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258649 · Cited by 3
A Survey on Digital Epidemiology: Networks and Models
Pastor-Escuredo, D., & Olmos, L. E. (2022) · Medical Research Archives · doi:10.18103/mra.v10i10.3175 · Cited by 5
Public Health Awareness Campaigns in Otolaryngology: Are We Making an Impact?
Kang, J. M., & Coelho, D. H. (2023) · Ear Nose & Throat Journal · doi:10.1177/01455613221149637 · Cited by 3
Venuleo, C., Marinaci, T., Savarese, G., & Venezia, A. (2023) · Culture in policy making · doi:10.1007/978-3-031-21995-5_8 · Cited by 3
Kalman, Y. M. (2011) · Journal of the Association for Information Systems · Cited by 3
Hernández, T. E., Ocampo, M. A., & Sabido, R. C. (2012) · PubMed · Cited by 3
An Online Real-Time System to Detect Risk for Infectious Diseases and Provide Early Alert
Fang, L., & Cao, Z. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22039-5_11 · Cited by 3
Performance Analysis of Wireless Body Area Sensor Analytics using Clustering Technique
Rakhee, Ohri, K., Saraswathi, R. V., & Vinita, L. J. (2019) · doi:10.1109/iccsp.2019.8697963 · Cited by 3
DiNer - On Building Multilingual Disease-News Profiler
Rustagi, S., & Patel, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-662-62199-8_5 · Cited by 3
Turvy, A. (2022) · JMIR Formative Research · doi:10.2196/40825 · Cited by 3
Ramanathan, A., Pullum, L., Hobson, T. C., Stahl, C. G., Steed, C. A., Quinn, S. P., Chennubhotla, C. S., & Valkova, S. (2015) · Frontiers in Public Health · doi:10.3389/fpubh.2015.00182 · Cited by 3
Disaster Network Science: Research and Applications
Hossain, L., & Feng, S. (2016) · Frontiers in Communication · doi:10.3389/fcomm.2016.00001 · Cited by 3
Co-query volume as a proxy for brand relatedness
Cho, S. (2018) · Industrial Management & Data Systems · doi:10.1108/imds-04-2017-0154 · Cited by 3
Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms
Huang, Y.-J., Su, C.-H., Chang, Y.-C., Ting, T.-H., Fu, T.-Y., Wang, R., Dai, H.-J., Chang, Y., Jonnagaddala, J., & Hsu, W. (2017) · Cited by 3
Molecular Tools Used in Medical and Veterinary Entomology
Nayduch, D., Fryxell, R. T., & Olafson, P. U. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814043-7.00028-5 · Cited by 3
Agrawal, A., & Gupta, A. (2020) · Journal of Microbiology and Infectious Diseases · doi:10.5799/jmid.839415 · Cited by 3
Donar, G. B. (2021) · Acibadem Universitesi Saglik Bilimleri Dergisi · doi:10.31067/acusaglik.851909 · Cited by 4
Reviewing topics of COVID-19 news articles: case study of CNN and China daily
Yuan, Y., Liu, K., & Wang, Y. (2022) · Aslib Journal of Information Management · doi:10.1108/ajim-05-2022-0264 · Cited by 3
Olsen, J. M. (2013) · ProQuest Demo Repository · Cited by 3
Pandemics in the Age of Twitter: A Content Analysis of the 2009 H1N1 Outbreak
Chew, C. (2010) · TSpace (University of Toronto) · Cited by 3
Time Series Methods and Ensemble Models to Nowcast Dengue at the State Level in BrazilPreprint
Kempfert, K., Martinez, K. M., Siraj, A. S., Conrad, J., Fairchild, G. C., Ziemann, A., Parikh, N., Osthus, D., Generous, N., Valle, S. Y. D., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.02483 · Cited by 3
Ngaphu, N. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671088.018 · Cited by 2
The Application of Artificial Intelligence in Addressing Biothreats
Yan, T., & Tian, J. (2025) · Disaster Medicine and Public Health Preparedness · doi:10.1017/dmp.2025.10120 · Cited by 2
Zhang, X., Lei, Z., Wang, Q., Wang, R., Luo, J., Jin, E., Wei, S., & Wang, Q. (2025) · Journal of Medical Internet Research · doi:10.2196/70232 · Cited by 2
Baichoo, S., Oladeji, O., Villareal, L., Diakabana, H., Okekunle, A. P., Marivate, V. N., Kaggwa, F., & Nsoesie, E. O. (2025) · Nature Communications · doi:10.1038/s41467-025-64766-4 · Cited by 2
Multi-Level Sociotechnical Systems Approach to Human-Centered AI
Schoenherr, J. R., & Michael, K. (2026) · Springer eBooks · doi:10.1007/978-981-95-3658-0_100 · Cited by 3
Interpreting Research in South Africa
Kotzé, H., & Wallmach, K. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671088.017 · Cited by 2
Sultanoğlu, T. E., & Ataoğlu, S. (2022) · Sağlık Bilimlerinde Değer · doi:10.33631/sabd.1055491 · Cited by 2
(2018) · Cambridge University Press eBooks · doi:10.1017/9781108575720 · Cited by 2
David, W., King‐Okoye, M., Mugambwa, I., & Garmendia-Doval, B. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-26754-3_13 · Cited by 2
Fundamentación ética del deber sobre el acceso abierto a fuentes de información científica
Valverde, A., & González-Miranda, J. A. (2024) · Revista Espiga · doi:10.22458/re.v23i47.5136 · Cited by 2
Search for a New Home: Refugee Stock and Google Search
Şanlıtürk, A. E., & Billari, F. C. (2024) · International Migration Review · doi:10.1177/01979183241275452 · Cited by 3
Suzuki, T. A. V., Cortez, D. G. T., Treyes, D. A. A., & Quinto, E. J. M. (2024) · International Conference on Education and Multimedia Technology (ICEMT) · doi:10.1145/3678726.3678769 · Cited by 2
HEDL: Deep learning multiple approaches for early detection of depression using sarcastic text
Kshirsagar, V. G., Pachpor, N. N., Brahme, A., Aapre, R., Suryawanshi, S. S., & Bhosale, D. G. (2025) · MethodsX · doi:10.1016/j.mex.2025.103370 · Cited by 2
A Framework for Interrogating Social Media Images to Reveal an Emergent Archive of War
Pleska, A., Hoskins, A., & Renaud, K. (2019) · International Journal of Humanities and Arts Computing · doi:10.3366/ijhac.2019.0236 · Cited by 3
Achilles Tendon Injury and Seasonal Variation: An Analysis Using Google Trends
Cha, Y.-S., Hwang, S.-M., & Yang, P.-J. (2019) · The Korean Journal of Sports Medicine · doi:10.5763/kjsm.2019.37.4.155 · Cited by 2
Wikipedia as a Transformative Multilingual Knowledge Resource
Pretorius, L., & Wolff, F. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671088.013 · Cited by 2
An Introduction to the Technological Basis of eHealth
Rinaldi, G. (2013) · Springer eBooks · doi:10.1007/978-88-470-5253-6_3 · Cited by 2
Operational Oceanography, End Users, and Social Network Sites: An Exploratory Analysis
Otero, P., Ruiz‐Villarreal, M., González-Nuevo, G., & Cabanas, J. M. O. (2014) · Oceanography · doi:10.5670/oceanog.2014.63 · Cited by 2
Prediction of online trade growth using search-ANFIS: Transactions on Taobao as examples
Jiyuan, W., Geng, P., & Dai, W. (2014) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2014.6891527 · Cited by 2
Kim, H., Laskowski, M., Moghadas, S. M., Sajad, A., & Asif, M. (2014) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2014.7051875 · Cited by 2
Ramanathan, A., Pullum, L., Steed, C. A., Chennubhotla, C., & Quinn, S. P. (2013) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 2
Data Analytics for Pharmaceutical Discoveries
Fakhraei, S., Onukwugha, E., & Getoor, L. C. (2015) · Cited by 2
Assessing E-Government Success Strategies using Internet Search Data
Boland, K. M., & McNutt, J. G. (2013) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-4666-4058-0.ch016 · Cited by 2
Automated Disease Outbreak Detection and Analysis
Tefrie, K. G., & Sohn, K.-A. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-0557-2_94 · Cited by 2
From Sewing Circles to Linky Parties: Women's Sewing Practices in the Digital Age
Russum, J. A. (2016) · Cited by 2
Developing and Measuring the Business Case for Health Information Technology
Unwin, D. W., Sanzogni, L., & Sandhu, K. (2017) · Advances in human services and public health (AHSPH) book series · doi:10.4018/978-1-5225-2262-1.ch015 · Cited by 2
Moon, H., Kim, K., Kim, S., & Park, J. (2017) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2017.79 · Cited by 2
A Patient Driven Approach to Patient Safety Interventions
Joseph, U. K. (2018) · Health Care Current Reviews · doi:10.4172/2375-4273.1000226 · Cited by 1
Big data aggregation in the case of heterogeneity: a feasibility study for digital health
Obinikpo, A. A., & Kantarcı, B. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-018-00904-3 · Cited by 3
Hayward, J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671088.016 · Cited by 2
The One-Health Approach to Infectious Disease Outbreaks Control
Rugarabamu, S. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.95759 · Cited by 2
Assessing E-Government Success Strategies using Internet Search Data
Boland, K. M., & McNutt, J. G. (2015) · IGI Global eBooks · doi:10.4018/978-1-4666-8358-7.ch056 · Cited by 2
Buckeridge, D. L. (2022) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-09108-7_13 · Cited by 2
The Metaverse in Prevention and Treatment of Disease
Sompura, D. U., & Tripathy, B. K. (2024) · Springer eBooks · doi:10.1007/978-3-031-60073-9_9 · Cited by 2
Predicting Popularity of Microblogs in Emerging Disease Event
Liu, J., Cao, Z., & Zeng, D. D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11538-2_1 · Cited by 2
The Role of Event-Based Biosurveillance in Biodefense
Hartley, D., Mui, W.-L., & Corley, C. D. (2019) · Springer eBooks · doi:10.1007/978-3-030-03053-7_3 · Cited by 2
Here's Looking at You: Public‐ Versus Elite‐Driven Models of Presidential Primary Elections
Swearingen, C., Stiles, E. A., & Finneran, K. (2019) · Social Science Quarterly · doi:10.1111/ssqu.12671 · Cited by 2
Conversational topics of social media messages associated with state-level mental distress rates
Bowen, D. A., Wang, J., Holland, K. M., Bartholow, B. N., & Sumner, S. A. (2020) · Journal of Mental Health · doi:10.1080/09638237.2020.1739251 · Cited by 2
Rapid validation of disease outbreak intelligence by small independent verification teams
Hatfill, S. J. (2020) · Intelligence & National Security · doi:10.1080/02684527.2020.1750149 · Cited by 2
African Language Resources for Knowledge Societies
Roux, J. C. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671088.012 · Cited by 3
Conceptualizing an Ecological Model of Google Search and Twitter Data in Public Health
Liang, B., & Wang, Y. (2021) · Human dynamics in smart cities · doi:10.1007/978-3-030-83010-6_10 · Cited by 2
(2018) · Wiley eBooks · doi:10.1002/9781118280249.refs · Cited by 2
Copyright Law and Data Protection Law
Berger, C. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108575720.005 · Cited by 2
Dolatabadi, E., Moyano, D., Bales, M., Spasojevic, S. S., Bhambhoria, R. V., Bhatti, J. A., Debnath, S., Hoell, N., Li, X., Leng, C., et al. (2022) · medRxiv · doi:10.1101/2022.12.14.22283419 · Cited by 2
Metadata
- Authors
- Brownstein, J. S., Freifeld, C. C., & Madoff, L. C.
- Affiliations
- Boston Children's Hospital (US)
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2009-05-21
- Volume
- 360
- Issue
- 21
- Pages
- 2153-2157
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC2917042
- OpenAlex
- W2160542585
- Cited by
- 819
- References
- 5
- Citations by year
- 2026: 40 · 2025: 31 · 2024: 33 · 2023: 29 · 2022: 40 · 2021: 60 · 2020: 81 · 2019: 57 · 2018: 80 · 2017: 68 · 2016: 59 · 2015: 51 · 2014: 54 · 2013: 53 · 2012: 35
- FWCI
- 18.66
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. National Library of Medicine (G08LM009776)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Social Sciences › Health › Social Media in Health Education
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Keywords
- disease surveillance, web-based surveillance, online health information, outbreak, internet search data, ProMED-mail
- MeSH terms
- Disease Outbreaks, Humans, Population Surveillance, Global Health, Information Storage and Retrieval, Internet, Communicable Diseases, Emerging
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