Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-17T04:09:46.691489Z
Paper Citation Record · LEDGER
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection
As of 29 July 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2511.20944.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-29T08:13:00.99439+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
34 of 34 outbound references displayed
- verified exact4
- verified fuzzy30
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 2abdf2c2-1aa0-404d-8ff3-2a427df7a88b · outbound
This paper cites Internet Crime Report 2023.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Internet Crime Report 2023
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.915295Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation c657739a-dfde-4bc6-a009-fcb5e20708c6 · outbound
This paper cites From ChatGPT to ThreatGPT: Impact of generative AI in cybersecurity and privacy.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection From ChatGPT to ThreatGPT: Impact of generative AI in cybersecurity and privacy
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.865167Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 9b392893-165e-45d1-bf13-13dc80f2f947 · outbound
This paper cites The sprawling reach of business email compro- mise.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection The sprawling reach of business email compro- mise
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.912094Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 7cb6e467-3b13-4c82-b40e-2d5c56ecbb3b · outbound
This paper cites Generative models for spear phishing.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Generative models for spear phishing
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.918235Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation d9fc0eaf-d15e-4f57-879c-1e9ad9be44d2 · outbound
This paper cites Bad characters: Imperceptible NLP attacks.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Bad characters: Imperceptible NLP attacks
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.901858Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 2d3fcddc-755f-49eb-a79c-f37725d057af · outbound
This paper cites Why phishing works.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Why phishing works
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.932192Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 875b73e0-0599-4d26-8477-feb21c1841e8 · outbound
This paper cites $n$-cluster tilting subcategories from gluing systems of representation-directed algebras.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection $n$-cluster tilting subcategories from gluing systems of representation-directed algebras
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T04:11:30.686127Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 0d098bd7-ab21-4b5e-8a8b-a23ca73795c1 · outbound
This paper cites DistilBERT.
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.922215Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 9708640c-b492-4c9d-a625-604559ccbff1 · outbound
This paper cites HotFlip: White-box adversarial examples for text classification.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection HotFlip: White-box adversarial examples for text classification
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.928940Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 3b2821df-1013-4abd-91ac-41fdc6681d76 · outbound
This paper cites TextAttack: A framework for adversarial attacks in NLP.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection TextAttack: A framework for adversarial attacks in NLP
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.948925Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation d4a8bc57-9cbd-4ea1-874e-7bff1d80c4ae · outbound
This paper cites Unicode technical standard #39: Unicode security mechanisms.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Unicode technical standard #39: Unicode security mechanisms
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.939353Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation e309d391-dbd7-4cb9-a5e8-8f7ef2a3a56a · outbound
This paper cites The foundations of cost-sensitive learning.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection The foundations of cost-sensitive learning
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.871699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation fbfe0e38-55b0-43b8-8c3d-4020b07a51c0 · outbound
This paper cites Detecting credit card fraud by decision trees and SVMs.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Detecting credit card fraud by decision trees and SVMs
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.905059Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation bb28917f-2360-4786-ba90-a4bcee4456e0 · outbound
This paper cites an unresolved cited work.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Unresolved cited work
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.888140Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 2932267b-fb56-413c-98ea-bbd12f04ef30 · outbound
This paper cites The Enron corpus.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection The Enron corpus
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.898754Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation ec9186d1-95d0-4c63-af2d-b913d9cf5a37 · outbound
This paper cites MITRE ATT&CK: Phishing for information (T1598).
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection MITRE ATT&CK: Phishing for information (T1598)
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.935800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 679c97b1-1a12-42cb-a565-bae5cad6c87d · outbound
This paper cites CatBoost.
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.878461Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 2f0eea5a-c612-4830-b382-46b0d455c6fe · outbound
This paper cites State of the phish 2024.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection State of the phish 2024
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.925398Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 18451c0c-efc0-4ffb-8ef7-85d60b30a792 · outbound
This paper cites PhishLang.
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.942710Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation e50a48f7-a096-4ce5-8ecf-655fdd75a179 · outbound
This paper cites Evaluating spam filters and stylometric detection.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Evaluating spam filters and stylometric detection
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.885515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation c52fcc9d-29e0-4c59-98ff-296fa524d991 · outbound
This paper cites Benchmarking NN robustness.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Benchmarking NN robustness
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.895905Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation e1bac2a2-7b3e-43d5-ad4d-9d09359b60fe · outbound
This paper cites Attention is all you need.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Attention is all you need
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.955404Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation d522459a-b3cf-4dd3-a34e-625cf4446ab9 · outbound
This paper cites Devlin et al., “BERT,” inProc.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Devlin et al., “BERT,” inProc
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.961784Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 5d11bcc0-2f24-4f57-9e0e-0d922c0ce053 · outbound
This paper cites Local curvature of maximally nondegenerate Radon-like transforms.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Local curvature of maximally nondegenerate Radon-like transforms
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T04:11:30.675136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 427b4f6f-5a0f-4325-b688-669e77d14dba · outbound
This paper cites Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T04:11:30.680784Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 9aa02bba-a075-41b9-b7ef-f7f65fe12119 · outbound
This paper cites Language models are few-shot learners.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Language models are few-shot learners
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.928697Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 7559388c-9141-4b4c-a5d3-bab5d41c014d · outbound
This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection RoBERTa: A Robustly Optimized BERT Pretraining Approach
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-05-17T04:11:30.690813Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation c2945e12-042b-414b-acf7-15427fef5577 · outbound
This paper cites DeBERTa.
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.911805Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 726c7d1d-04d5-4c52-a520-dd107b8584e1 · outbound
This paper cites Decoupled weight decay regularization.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Decoupled weight decay regularization
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.917951Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation da68499e-4a1e-4bd9-b989-9c22623b1e5f · outbound
This paper cites Scikit-learn: Machine learning in Python.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Scikit-learn: Machine learning in Python
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.945521Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 8ef43dfa-9177-4f07-9add-61b081f7468f · outbound
This paper cites spaCy 2.
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.952373Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation d5245702-d409-4964-85ca-0acb79b425e4 · outbound
This paper cites Akiba et al., “Optuna,” inProc.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Akiba et al., “Optuna,” inProc
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.904792Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 0f121def-aae6-4a03-bd65-4731a5a35097 · outbound
This paper cites Interpreting model predictions.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Interpreting model predictions
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.958352Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Observation 7b9e080b-b3fe-4344-9059-feb0014567f4 · outbound
This paper cites Verification of forecasts.
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Verification of forecasts
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T04:11:30.885709Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-29T08:13:00.99439+00:00.
Pith citing papers
No inbound Pith citation observations are available.