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Cyberthreat Detection from Twitter using Deep Neural Networks
Open accessAbstract
To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.
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“Studies justify these features per QA1 for capturing subtle behavioral cues, such as sentiment shifts or entity relationships, that traditional metrics miss [17,70].”
From A systematic review on insider threat detection using natural language processing (2025) · cites this paper as [70]
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
(2014) · Cited by 33,419
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
Research works printed but not linked: 1
- abadi, 2016, TensorFlow: A System for Largescale Machine Learning, Proc of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI)
Cited by
Sorted by citations
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Kaur, R., Gabrijelčič, D., & Klobučar, T. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101804 · Cited by 615
Cyber Threat Intelligence Mining for Proactive Cybersecurity Defense: A Survey and New Perspectives
Sun, N., Ding, M., Jiang, J., Xu, W., Mo, X., Tai, Y., & Zhang, J. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3273282 · Cited by 367
CSKG4APT: A Cybersecurity Knowledge Graph for Advanced Persistent Threat Organization Attribution
Ren, Y., Xiao, Y., Zhou, Y., Zhang, Z., & Tian, Z. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3175719 · Cited by 234
LLM-TIKG: Threat intelligence knowledge graph construction utilizing large language model
Hu, Y., Zou, F., Han, J., Sun, X., & Wang, Y. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103999 · Cited by 173
Processing tweets for cybersecurity threat awareness
Alves, F., Bettini, A., Ferreira, P., & Bessani, A. (2020) · Information Systems · doi:10.1016/j.is.2020.101586 · Cited by 83
Self-citationA survey on cybersecurity knowledge graph construction
Zhao, X., Jiang, R., Han, Y., Li, A., & Peng, Z. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103524 · Cited by 78
CDTier: A Chinese Dataset of Threat Intelligence Entity Relationships
Zhou, Y., Ren, Y., Yi, M., Xiao, Y., Tan, Z., Moustafa, N., & Tian, Z. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3240411 · Cited by 64
Data and knowledge-driven named entity recognition for cyber security
Gao, C., Zhang, X., & Liu, H. (2021) · Cybersecurity · doi:10.1186/s42400-021-00072-y · Cited by 67
Evaluation of LLM-based chatbots for OSINT-based Cyber Threat Awareness
Shafee, S., Bessani, A., & Ferreira, P. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125509 · Cited by 101
Self-citationAutomated Emerging Cyber Threat Identification and Profiling Based on Natural Language Processing
Marinho, R., & Holanda, R. (2023) · IEEE Access · doi:10.1109/access.2023.3260020 · Cited by 50
CTI View: APT Threat Intelligence Analysis System
Zhou, Y., Tang, Y., Yi, M., Chuan-yu, X., & Lu, H. (2022) · Security and Communication Networks · doi:10.1155/2022/9875199 · Cited by 55
APTNER: A Specific Dataset for NER Missions in Cyber Threat Intelligence Field
Wang, X., He, S., Xiong, Z., Wei, X., Jiang, Z., Chen, S., & Jiang, J. (2022) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd54268.2022.9776031 · Cited by 54
Threatening Language Detection and Target Identification in Urdu Tweets
Amjad, M., Ashraf, N., Zhila, A., Sidorov, G., Zubiaga, A., & Gelbukh, A. F. (2021) · IEEE Access · doi:10.1109/access.2021.3112500 · Cited by 48
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Altalhi, S., & Gutub, A. A. A. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02789-z · Cited by 45
Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification
Le, B. D., Wang, G., Nasim, M., & Babar, M. A. (2019) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2019.00058 · Cited by 44
TriCTI: an actionable cyber threat intelligence discovery system via trigger-enhanced neural network
Liu, J., Yan, J., Jiang, J., He, Y., Wang, X., Jiang, Z., Yang, P., & Li, N. (2022) · Cybersecurity · doi:10.1186/s42400-022-00110-3 · Cited by 43
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Taherdoost, H. (2023) · Algorithms · doi:10.3390/a16060271 · Cited by 47
Shin, H., Kwon, H.-Y., & Ryu, S. (2020) · Electronics · doi:10.3390/electronics9091527 · Cited by 43
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Stutz, D., de Assis, J. T., Laghari, A. A., Khan, A. A.-M., Andreopoulos, N., Terziev, P. A., Deshpande, A. B., Kulkarni, D. S., & Grata, E. G. H. (2024) · doi:10.1002/9781394196470.ch11 · Cited by 40
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Gao, C., Zhang, X., Han, M., & Liu, H. (2021) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2000286 · Cited by 43
NLP-based techniques for Cyber Threat Intelligence
Arazzi, M., Arikkat, D. R., Nicolazzo, S., Nocera, A., A., R. R. K., Vinod, P., & Conti, M. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100765 · Cited by 75
Towards end-to-end Cyberthreat Detection from Twitter using Multi-Task Learning
Dionísio, N., Alves, F., Ferreira, P., & Bessani, A. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207159 · Cited by 40
Self-citationIntegrating AI in security information and event management for real time cyber defense
Khan, S. A., Dilshad, N., Ahmad, N., Noor, S., & AlQahtani, S. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-19689-x · Cited by 37
Pattnaik, N., Li, S., & Nurse, J. R. C. (2022) · Computers & Security · doi:10.1016/j.cose.2022.103008 · Cited by 42
Browne, T. O., Abedin, M., & Chowdhury, M. J. M. (2024) · International Journal of Information Security · doi:10.1007/s10207-024-00868-2 · Cited by 34
Threatening URDU Language Detection from Tweets Using Machine Learning
Mehmood, A., Farooq, M. S., Naseem, A., Rustam, F., Villar, M. G., Rodríguez, C. L., & Ashraf, I. (2022) · Applied Sciences · doi:10.3390/app122010342 · Cited by 28
AECR: Automatic attack technique intelligence extraction based on fine-tuned large language model
Chen, M., Zhu, K., Lu, B., Li, D., Yuan, Q., & Zhu, Y. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104213 · Cited by 31
Mendsaikhan, O., Hasegawa, H., Yamaguchi, Y., & Shimada, H. (2020) · IEEE Access · doi:10.1109/access.2020.3027321 · Cited by 25
#Twiti: Social Listening for Threat Intelligence
Shin, H., Shim, W., Kim, S., Lee, S., Kang, Y. G., & Hwang, Y. H. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449797 · Cited by 29
Cyberattack detection model using community detection and text analysis on social media
Park, J.-H., & Kwon, H.-Y. (2021) · ICT Express · doi:10.1016/j.icte.2021.12.003 · Cited by 25
Mulahuwaish, A., Qolomany, B., Gyorick, K., Abdo, J. B., Aledhari, M., Qadir, J., Carley, K. M., & Al‐Fuqaha, A. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100668 · Cited by 35
CySecAlert: An Alert Generation System for Cyber Security Events Using Open Source Intelligence Data
Riebe, T., Wirth, T., Bayer, M., Kühn, P., Kaufhold, M., Knauthe, V., Guthe, S., & Reuter, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86890-1_24 · Cited by 22
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Alves, F., Andongabo, A., Gashi, I., Ferreira, P., & Bessani, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58951-6_11 · Cited by 21
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Iorga, D., Corlatescu, D., Grigorescu, O., Sandescu, C., Dascalu, M., & Rughinis, R. (2020) · 2020 19th RoEduNet Conference: Networking in Education and Research (RoEduNet) · doi:10.1109/roedunet51892.2020.9324852 · Cited by 20
Zhang, T., Lin, H., Ren, Y., Yang, Z., Wang, J., Duan, X., & Xu, B. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.007 · Cited by 20
Riebe, T., Bäumler, J., Kaufhold, M., & Reuter, C. (2023) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-022-09453-4 · Cited by 21
Adversarial Active Learning for Named Entity Recognition in Cybersecurity
Li, T., Hu, Y., Ju, A., & Hu, Z. (2020) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2020.012023 · Cited by 20
Log Files Analysis For Network Intrusion Detection
Brandão, A., & Georgieva, P. (2020) · IEEE International Conference on Intelligent Systems · doi:10.1109/is48319.2020.9199976 · Cited by 19
A Novel Threat Intelligence Information Extraction System Combining Multiple Models
Li, Y., Guo, Y., Chen, F., Liu, Y., & Chen, Q. (2022) · Security and Communication Networks · doi:10.1155/2022/8477260 · Cited by 18
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Alves, P. M. D. M. R., Filho, G. P. R., & Gonçalves, V. P. (2022) · Workshop on Communication Networks and Power Systems (WCNPS) · doi:10.1109/wcnps56355.2022.9969697 · Cited by 19
Exploring topic models to discern cyber threats on Twitter: A case study on Log4Shell
Wang, Y., Bashar, M. A., Chandramohan, M., & Nayak, R. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200280 · Cited by 16
Kristiansen, L.-M., Agarwal, V., Franke, K., & Shah, R. S. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378393 · Cited by 16
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Haile, A. T., Abebe, S. L., & Melaku, H. M. (2025) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2025.3580235 · Cited by 14
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Vitulyova, Y. S., Бабенко, Т. В., Kolesnikova, K. V., Kiktev, N. A., & Abramkina, O. (2025) · Computers · doi:10.3390/computers14080301 · Cited by 22
ExpSeeker: extract public exploit code information from social media
Du, Y., Huang, C., Liang, G., Fu, Z., Li, D., & Ding, Y. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04178-9 · Cited by 13
Design of a Classification Model for a Twitter-Based Streaming Threat Monitor
Alves, F., Ferreira, P., & Bessani, A. (2019) · International Conference on Dependable Systems and Networks workshops · doi:10.1109/dsn-w.2019.00010 · Cited by 14
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Aranovich, R., Wu, M., Yu, D., Katsy, K., Ahmadnia, B., Bishop, M., Filkov, V., & Sagae, K. (2021) · New Security Paradigms Workshop · doi:10.1145/3498891.3501259 · Cited by 9
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A Twitter-based Software Vulnerability Alert Framework using Natural Language Processing
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A systematic review on insider threat detection using natural language processing
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Brown, A. F., Garg, S., Montgomery, J., & Ujjwal, K. C. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.01.020 · Cited by 7
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A Framework for Unsupervised Classificiation and Data Mining of Tweets about Cyber VulnerabilitiesPreprint
Alperin, K., Joback, E., Shing, L., & Elkin, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.11695 · Cited by 9
Non-stationary Continuum-armed Bandits for Online Hyperparameter Optimization
Lu, S., Zhou, Y., Shi, J.-C., Zhu, W., Yu, Q., Chen, Q.-G., Da, Q., & Zhang, L. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498396 · Cited by 6
Threat Action Extraction using Information Retrieval
Chen, C.-M., Kan, J.-Y., Ou, Y.-H., Cai, Z.-X., & Guan, A. (2021) · doi:10.5121/csit.2021.110702 · Cited by 6
Cyber Threat Detection on Twitter Using Deep Learning Techniques: IDCNN and BiLSTM Integration
Alsodi, O., Zhou, X., Gururajan, R., Shrestha, A., & Btoush, E. (2024) · International Conference on Advanced Cloud and Big Data · doi:10.1109/cbd65573.2024.00073 · Cited by 7
CyberDualNER: A Dual-Stage Approach for Few-Shot Named Entity Recognition in Cybersecurity
Zheng, C., Lu, C., Li, C., Zheng, Z., & Pan, L. (2025) · Electronics · doi:10.3390/electronics14091791 · Cited by 7
A Survey on Detection of cybersecurity threats on Twitter using deep learning
Alsodi, O., Zhou, X., Gururajan, R., & Shrestha, A. (2021) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc53957.2021.9635406 · Cited by 5
Mohamad, A. M., Yong, F. Y. Y., Aziz, N. A., & Norhisham, S. (2022) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet54531.2022.9824944 · Cited by 5
CyTIE: Cyber Threat Intelligence Extraction with Named Entity Recognition
Aravind, P. C., Arikkat, D. R., Krishnan, A. S., Tesneem, B., Sebastian, A., Dev, M. J., Aswathy, K. R., Rehiman, K. A. R., & Vinod, P. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-59100-6_13 · Cited by 5
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Smolyakova, S., Khodayarseresht, E., & Majumdar, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3413351 · Cited by 5
TI-Prompt: Towards a Prompt Tuning Method for Few-shot Threat Intelligence Twitter Classification
You, Y., Jiang, Z., Zhang, K., Jiang, J., Wang, X., Zhang, Z., Wang, S., & Feng, H. (2022) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac54236.2022.00046 · Cited by 6
Context-Aware Entity-Relation Extraction Pipeline for Threat Intelligence Knowledge GraphsPreprint
Mouiche, I., Merbouh, H., & Saad, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5152241 · Cited by 5
CyberNER-LLM: Cyber Threat Intelligence Named Entity Recognition With Large Language Model
Liu, X., Lin, W., & Ding, Z. (2026) · Lecture Notes in Computer Science · doi:10.1007/978-981-95-3543-9_28 · Cited by 4
Enhancing security information and event management with W2V-BERT-based real-time threat detection
Khan, S., Dilshad, N., Ahmad, N., & AlQahtani, S. A. (2026) · Scientific Reports · doi:10.1038/s41598-026-49610-z · Cited by 4
Useful Cyber Threat Intelligence Relation Retrieval Using Transfer Learning
Chen, C.-M., Hsu, F.-H., & Hwang, J. (2023) · European Interdisciplinary Cybersecurity Conference (EICC) · doi:10.1145/3590777.3590784 · Cited by 5
The Role of Datasets when Building Next Generation Intrusion Detection Systems
Nazarov, A. N., Константинович, С. А., & Voronkov, I. M. (2019) · Wave Electronics and its Application in Information and Telecommunication Systems (WECONF) · doi:10.1109/weconf.2019.8840124 · Cited by 4
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Marinho, R., & Filho, R. H. (2023) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom58365.2023.10271627 · Cited by 4
Legapriyadharshini, N., Juliet, A. H., & Devi, S. R. (2025) · doi:10.1109/iccsp64183.2025.11089275 · Cited by 3
PROMPT-BART: A Named Entity Recognition Model Applied to Cyber Threat Intelligence
Feng, X., He, S., Wei, X., Liu, R., Yue, H., & Wang, X. (2025) · Applied Sciences · doi:10.3390/app151810276 · Cited by 3
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Hao, Z., Li, C., Fu, X., Luo, B., & Du, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70903-6_4 · Cited by 3
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Sezgin, A., & Boyacı, A. (2023) · Fırat Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.35234/fumbd.1217219 · Cited by 2
Prabakaran, S., Muthunambu, N. K., & Jeyaraman, N. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3695251 · Cited by 2
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Singh, V. K., Mohankumar, P., & Kamal, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-3102-5_27 · Cited by 2
AspIOC: Aspect-Enhanced Deep Neural Network for Actionable Indicator of Compromise Recognition
Wang, S., Lang, B., Xiao, N., & Chen, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22390-7_24 · Cited by 2
Adebiyi, M. O., Ajayi, M., Osang, F. B., & Adebiyi, A. A. (2023) · International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG) · doi:10.1109/seb-sdg57117.2023.10124615 · Cited by 2
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Metadata
- Authors
- Dionisio, N., Alves, F., Ferreira, P. M., & Bessani, A.
- Affiliations
- University of Lisbon (PT)
- Venue
- 2019 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2019-07
- Pages
- 1-8
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2934951104
- Cited by
- 126
- References
- 31
- Citations by year
- 2026: 6 · 2025: 23 · 2024: 17 · 2023: 28 · 2022: 18 · 2021: 17 · 2020: 13 · 2019: 4
- FWCI
- 11.23
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Horizon 2020 Framework Programme (700692)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- Twitter, open source intelligence, named entity recognition, bidirectional LSTM, convolutional neural networks, security alerts, indicators of compromise, IT infrastructure monitoring, deep neural network
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 ›
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