Public attitudes toward chatgpt on twitter: sentiments, topics, and occupations
Open accessThe 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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
We used a single LLM (ChatGPT-4o) as the basis for AI-assisted workflows.
“Fourth, we used a single LLM (ChatGPT-4o) as the basis for AI-assisted workflows, considering its popularity among general and non-expert users [34, 39].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [39]
The Introduction reports that ChatGPT gained widespread attention and reached an estimated 100 million monthly active users in January 2023, supporting its general popularity. The paper does not establish popularity specifically among non-expert users or for ChatGPT-4o. The citing authors’ choice to use one model is their own methodological decision, not a finding attributed to this source. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Interrater reliability: The kappa statistic
McHugh, M. L. (2012) · Biochemia Medica · doi:10.11613/bm.2012.031 · Cited by 19,877 · see citing works
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087 · see citing works
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584 · see citing works
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · see citing works
Kung, T. H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., et al. (2022) · doi:10.1101/2022.12.19.22283643 · Cited by 4,115 · see citing works
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00237-x · Cited by 2,003 · cited 1× · see citing works
“Others focused on its potential in specific domains, such as education and healthcare (Dwivedi et al. 2023; Biswas 2023; Tlili et al. 2023; Dempere et al. 2023; Shoufan 2023).”
Competition-level code generation with AlphaCode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · Science · doi:10.1126/science.abq1158 · Cited by 1,043 · see citing works
TruthfulQA: Measuring How Models Mimic Human Falsehoods
Lin, S., Hilton, J., & Evans, O. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.229 · Cited by 904 · see citing works
TruthfulQA: Measuring How Models Mimic Human Falsehoods
Lin, S., Hilton, J., & Evans, O. (2021) · arXiv · doi:10.48550/arxiv.2109.07958 · Cited by 899 · see citing works
ChatGPT: Jack of all trades, master of none
Kocoń, J., Cichecki, I., Kaszyca, O., Kochanek, M., Szydło, D., Baran, J., Bielaniewicz, J., Gruza, M., Janz, A., Kanclerz, K., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101861 · Cited by 758 · see citing works
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification
Barbieri, F., Camacho-Collados, J., Anke, L. E., & Neves, L. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.148 · Cited by 756 · see citing works
Role of Chat GPT in public health
Biswas, S. S. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03172-7 · Cited by 673 · see citing works
Tools such as ChatGPT threaten transparent science; here are our ground rules for their use
(2023) · Nature · doi:10.1038/d41586-023-00191-1 · Cited by 605 · see citing works
Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse?
O’Connor, S., & ChatGPT (2023) · Nurse Education in Practice · doi:10.1016/j.nepr.2022.103537 · Cited by 585 · see citing works
ChatGPT Goes to Law SchoolPreprint
Choi, J. H., Hickman, K. E., Monahan, A., & Schwarcz, D. B. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4335905 · Cited by 522 · see citing works
The impact of ChatGPT on higher education
Dempere, J., Modugu, K., Hesham, A., & Ramasamy, L. K. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1206936 · Cited by 519 · cited 1× · see citing works
“Others focused on its potential in specific domains, such as education and healthcare (Dwivedi et al. 2023; Biswas 2023; Tlili et al. 2023; Dempere et al. 2023; Shoufan 2023).”
A Categorical Archive of ChatGPT Failures
Borji, A. (2023) · doi:10.21203/rs.3.rs-2895792/v1 · Cited by 514 · see citing works
Exploring Students’ Perceptions of ChatGPT: Thematic Analysis and Follow-Up Survey
Shoufan, A. (2023) · IEEE Access · doi:10.1109/access.2023.3268224 · Cited by 499 · cited 1× · see citing works
“Others focused on its potential in specific domains, such as education and healthcare (Dwivedi et al. 2023; Biswas 2023; Tlili et al. 2023; Dempere et al. 2023; Shoufan 2023).”
“What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter
Taecharungroj, V. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010035 · Cited by 489 · see citing works
Ethical and social risks of harm from Language Models
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.04359 · Cited by 477 · see citing works
Cheng, Y., & Jiang, H. (2021) · Journal of Product & Brand Management · doi:10.1108/jpbm-05-2020-2907 · Cited by 439 · see citing works
Lee, M., Liang, P., & Yang, Q. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502030 · Cited by 345 · see citing works
ChatGPT: Fundamentals, Applications and Social Impacts
Abdullah, M., Madain, A., & Jararweh, Y. (2022) · 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams58071.2022.10062688 · Cited by 310 · see citing works
Co-Writing with Opinionated Language Models Affects Users’ Views
Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., & Naaman, M. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581196 · Cited by 265 · cited 1× · see citing works
“From a societal standpoint, it becomes alarmingly disturbing considering the bias and discrimination inherent to LLMs from their training datasets (Jakesch et al. 2023; Lin et al. 2021; Weidinger et al. 2021; Zhuo et al. 2023; Abid et al. 2021).”
TimeLMs: Diachronic Language Models from Twitter
Loureiro, D., Barbieri, F., Neves, L., Espinosa Anke, L., & Camacho-collados, J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2022.acl-demo.25 · Cited by 263 · see citing works
ChatGPT in education: global reactions to AI innovations
Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., & Gerjets, P. (2023) · Scientific Reports · doi:10.1038/s41598-023-42227-6 · Cited by 242 · cited 2× · see citing works
“Studies that focused on specific areas like cybersecurity (Okey et al. 2023), healthcare (Praveen and Vajrobol 2023), or education (Li et al. 2023; Fütterer et al. 2023) examined tweets with relatively longer time spans, ranging from 2 to 4 months.”
Mirowski, P., Mathewson, K. W., Pittman, J., & Evans, R. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581225 · Cited by 240 · cited 1× · see citing works
“It has captured people’s imagination about a revolutionized future of programming (Chen et al. 2021; Li et al. 2022), writing (Mirowski et al. 2022; Lee et al. 2022), and many more (Kocoń et al. 2023; Fatouros et al. 2023).”
Rapamycin in the context of Pascal's Wager: generative pre-trained transformer perspective
Generative Pre-trained Transformer, C., & Zhavoronkov, A. (2022) · Oncoscience · doi:10.18632/oncoscience.571 · Cited by 203 · see citing works
ChatGPT in education: a discourse analysis of worries and concerns on social media
Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., & Hemphill, L. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12256-9 · Cited by 201 · see citing works
Understanding public attitudes to technology
Frewer, L. J., Howard, C., & Shepherd, R. (1998) · Journal of Risk Research · doi:10.1080/136698798377141 · Cited by 196 · see citing works
Large language models associate Muslims with violence
Abid, A., Farooqi, M., & Zou, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00359-2 · Cited by 193 · see citing works
Impact of ChatGPT on medical chatbots as a disruptive technology
Chow, J. C. L., Sanders, L., & Li, K. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1166014 · Cited by 188 · see citing works
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond
Barbieri, F., Espinosa-Anke, L., & Camacho-Collados, J. (2022) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4fhqtpvii3vg · Cited by 165 · see citing works
Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity
Zhuo, T. Y., Huang, Y., Chen, C., & Xing, Z. (2023) · arXiv · doi:10.48550/arxiv.2301.12867 · Cited by 164 · cited 1× · see citing works
“From a societal standpoint, it becomes alarmingly disturbing considering the bias and discrimination inherent to LLMs from their training datasets (Jakesch et al. 2023; Lin et al. 2021; Weidinger et al. 2021; Zhuo et al. 2023; Abid et al. 2021).”
Chatgpt: a meta-analysis after 2.5 months
Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., & Eger, S. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100541 · Cited by 115 · see citing works
de Cosmo, L. M., Piper, L., & Di Vittorio, A. (2021) · Italian Journal of Marketing · doi:10.1007/s43039-021-00020-1 · Cited by 82 · see citing works
Investigating ChatGPT and cybersecurity: A perspective on topic modeling and sentiment analysis
Okey, O. D., Udo, E. U., Rosa, R. L., Rodríguez, D. Z., & Kleinschmidt, J. H. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103476 · Cited by 81 · see citing works
Truth, Lies, and Automation: How Language Models Could Change Disinformation
Center for Security and Emerging Technology, Buchanan, B., Lohn, A., Musser, M., & Sedova, K. (2021) · doi:10.51593/2021ca003 · Cited by 74 · see citing works
Analyzing the User's Sentiments of ChatGPT Using Twitter Data
Korkmaz, A., Aktürk, C., & Talan, T. (2023) · Iraqi Journal for Computer Science and Mathematics · doi:10.52866/ijcsm.2023.02.02.018 · Cited by 64 · see citing works
Praveen, S. V., & Vajrobol, V. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03222-0 · Cited by 62 · see citing works
Teaching Models to Express Their Uncertainty in Words
Lin, S., Hilton, J., & Evans, O. (2022) · arXiv · doi:10.48550/arxiv.2205.14334 · Cited by 59 · see citing works
(2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00613-9 · Cited by 48 · see citing works
ChatGPT: Disruptive Educational Technology
Frith, K. H. (2023) · Nursing Education Perspectives · doi:10.1097/01.nep.0000000000001129 · Cited by 43 · see citing works
Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models
Zhou, K., Jurafsky, D., & Hashimoto, T. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.335 · Cited by 38 · see citing works
FOLIO: Natural Language Reasoning with First-Order Logic
Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Zhou, W., Coady, J., Peng, D., Qiao, Y., Benson, L., et al. (2022) · arXiv · doi:10.48550/arxiv.2209.00840 · Cited by 35 · see citing works
Sentiment Analysis of Using ChatGPT in Education
Tubishat, M., Al-Obeidat, F. N., & Shuhaiber, A. (2023) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets58706.2023.10215977 · Cited by 29 · see citing works
Tounsi, A., Elkefi, S., & Layeb, S. B. (2023) · IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) · doi:10.1109/ic_aset58101.2023.10150870 · Cited by 13 · see citing works
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond
Barbieri, F., Anke, L. E., & Camacho-Collados, J. (2021) · arXiv · doi:10.48550/arxiv.2104.12250
Flagged first, then by citations
Research works printed but not linked: 8
- Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) · arXiv
- abq1158 · DOI · Paper page
- Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586-1597 (2018)
- Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: " i think this is the most disruptive technology": Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) · arXiv
- Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993-1022 (2003)
- Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21-29 (1997). IEEE
- Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) · arXiv
- Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) · arXiv
Cited by
Sorted by citations
Cherrez-Ojeda, I., Gallardo-Bastidas, J. C., Robles-Velasco, K., Osorio, M. F., Velez Leon, E. M., Leon Velastegui, M., Pauletto, P., Aguilar-Díaz, F. C., Squassi, A., González Eras, S. P., et al. (2024) · JMIR Medical Education · doi:10.2196/51757 · Cited by 46 · see citing works
Urbina, J. T., Vu, P. D., & Nguyen, M. V. (2024) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2024.08.014 · Cited by 31 · see citing works
Dissecting a social bot powered by generative AI: anatomy, new trends and challenges
López-Joya, S., Diaz-Garcia, J. A., Ruiz, M. D., & Martín-Bautista, M. J. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01410-5 · Cited by 13 · see citing works
Cvetkovic, A., Savolainen, I., Koike, M., & Oksanen, A. (2025) · Telematics and Informatics Reports · doi:10.1016/j.teler.2025.100220 · Cited by 13 · see citing works
Tao, Y., & Shen, Q. (2025) · PLoS ONE · doi:10.1371/journal.pone.0334331 · Cited by 7 · see citing works
Xu, Z., & Zhang, X. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2508311 · Cited by 6 · see citing works
Shaikh, S., Yayilgan, S. Y., Abomhara, M., & Zoto, E. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01434-x · Cited by 8 · see citing works
AI competence and sentiment: a mixed-methods study of attitudes and open-ended reflections
Lāma, G., & Lastovska, A. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1658791 · Cited by 7 · see citing works
Panda, M., Singh, K., Raj, A., & Puri, R. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00730-x · Cited by 7 · see citing works
Karataş, M., & Cutright, K. M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2407917121 · Cited by 4 · see citing works
Lin, Z., Huang, Q., Zhong, J., Xie, J., Wang, Y., & Weiying, H. (2026) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01546-4 · Cited by 4 · see citing works
Tang, Z., Ma, W., Bai, Z., Liang, J., & Xie, Y. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1839898 · Cited by 4 · see citing works
Ara, A., Mujahid, M., Al‐Rasheed, A., Al-Otaibi, S. T., & Saba, T. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.065566 · Cited by 2 · see citing works
Research on the Enhanced DistilBERT Model for NLP-Based Text Analysis
Wang, D., Long, Y., & Huang, X. (2025) · Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) · doi:10.1109/acctcs66275.2025.00124 · Cited by 2 · see citing works
Rauchfleisch, A., Suarez, J. P., Sales, N. M., & Jungherr, A. M. (2025) · Telematics and Informatics · doi:10.1016/j.tele.2025.102344 · Cited by 2 · see citing works
When GenAI writes public foresight reports: Effects on convincing citizens
Zumbrunn, L., & Guenduez, A. A. (2025) · Public Policy and Administration · doi:10.1177/09520767251403049 · Cited by 4 · see citing works
Raees, M., Iwamoto, Y., Papangelis, K., Heard, J., & Tigwell, G. W. (2026) · arXiv · doi:10.48550/arxiv.2602.08937 · Cited by 1 · see citing works
“The figure highlights that distortions reduce the ability of observers to guess passwords unless they already know the content.””
Leveraging LLMs for Integrated Sentiment and Topic Analysis on African Social Media
Sibitenda, H., Hu, R., Rundensteiner, E. A., Diattara, A., Traore, A., & Ba, C. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00221 · Cited by 1 · see citing works
Jonathan, A., Halim, C. K., Wulandhari, L. A., & Nabiilah, G. Z. (2025) · International Conference on Smart Computing, IoT and Machine Learning (SIML) · doi:10.1109/siml65326.2025.11081155 · Cited by 1 · see citing works
Türkiye’de ChatGPT algısı: Bert modeli ile duygu analizi
GAZAZ, D. C., & Ayvaz, S. (2024) · Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi · doi:10.31795/baunsobed.1470846 · Cited by 1 · see citing works
The Impact of Large Language Models on Cybersecurity
Moongela, H., & Mayayise, T. O. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-96262-2_10 · Cited by 1 · see citing works
SentimentFusion-Net: A Hybrid ML-DL Framework for Benchmarking ChatGPT User Opinions on Twitter
Bhuiyan, M. T. A., Rahman, M. A., Ahmed, M. J., Khan, M. F. I. K., & Masum, A. K. M. (2025) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit68739.2025.11491142
Chakraborty, S., Gill, S. K., & Verma, M. K. (2026) · Library Hi Tech · doi:10.1108/lht-05-2025-0112
Theilen, R., Kramer, T., Scharffenberg, M., Jung, I.-C., Zerlik, M., de Abreu, M. G., Sepehr, S., KOCH, T., Sedlmayr, B., Huhle, R., et al. (2026) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-026-01464-6
Developing and validating the attitudes toward large language models scale
Liebherr, M., Almourad, M. B., Alshakhsi, S. A. A., Montag, C., Xu, G., Ali, R., & Yankouskaya, A. (2026) · Cogent Psychology · doi:10.1080/23311908.2026.2718273
Demirel, S. (2026) · SELÇUK ÜNİVERSİTESİ İLETİŞİM FAKÜLTESİ AKADEMİK DERGİSİ · doi:10.18094/josc.1806626
- Authors
- Koonchanok, R., Pan, Y., & Jang, H.
- Affiliations
- Indiana University Indianapolis (US)
- Corresponding author
- Koonchanok, Ratanond
- Venue
- Social Network Analysis and Mining
- Published
- 2024-05-20
- Volume
- 14
- Issue
- 1
- ISSN
- 1869-5450 (ISSN-L)
- 1869-5469
- OpenAlex
- W4398140700
- Cited by
- 25
- References
- 45
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 5
- FWCI
- 3.70
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Social Network Analysis and Mining (publishedVersion)
- IUScholarWorks (Indiana University) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- ChatGPT, public attitudes, large language models, chatbots, Twitter, sentiment analysis, topic modeling, occupational differences, societal impact of AI, cybersecurity, Google Bard, OpenAI
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
