The DOI matches its Crossref record.
The Enron Corpus: A New Dataset for Email Classification Research
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
Cited by
The “majority illusion” in social networks
Lerman, K., Yan, X., & Wu, X.-Z. (2016) · PLOS ONE · doi:10.1371/journal.pone.0147617 · Cited by 669
Computer-Assisted Text Analysis for Comparative Politics
Lucas, C., Nielsen, R. A., Roberts, M. E., Stewart, B. M., Storer, A., & Tingley, D. (2015) · Political Analysis · doi:10.1093/pan/mpu019 · Cited by 366
Explainable Artificial Intelligence in CyberSecurity: A Survey
Capuano, N., Fenza, G., Loia, V., & Stanzione, C. (2022) · IEEE Access · doi:10.1109/access.2022.3204171 · Cited by 330
Surveying Stylometry Techniques and Applications
Neal, T., Sundararajan, K., Fatima, A., Yan, Y., Xiang, Y., & Woodard, D. (2018) · ACM Computing Surveys · doi:10.1145/3132039 · Cited by 222
Interacting meaningfully with machine learning systems: Three experiments
Stumpf, S., Rajaram, V., Li, L., Wong, W.-K., Burnett, M., Dietterich, T., Sullivan, E., & Herlocker, J. (2009) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2009.03.004 · Cited by 184
What Does it Mean for a Language Model to Preserve Privacy?
Brown, H., Lee, K., Mireshghallah, F., Shokri, R., & Tramèr, F. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3534642 · Cited by 165
A hybrid learning system for recognizing user tasks from desktop activities and email messages
Shen, J., Li, L., Dietterich, T. G., & Herlocker, J. L. (2006) · Proceedings of the 11th international conference on Intelligent user interfaces · doi:10.1145/1111449.1111473 · Cited by 109
Buschek, D., Zürn, M., & Eiband, M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445372 · Cited by 108
Privacy Amplification by Iteration
Feldman, V., Mironov, I., Talwar, K., & Thakurta, A. (2018) · 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS) · doi:10.1109/focs.2018.00056 · Cited by 80
Property Inference from Poisoning
Mahloujifar, S., Ghosh, E., & Chase, M. (2022) · 2022 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp46214.2022.9833623 · Cited by 67
(2024) · doi:10.1109/sp54263.2024.00182 · Cited by 62
Employee profiling via aspect-based sentiment and network for insider threats detection
Soh, C., Yu, S., Narayanan, A., Duraisamy, S., & Chen, L. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.043 · Cited by 61
Complementing Text Entry Evaluations with a Composition Task
Vertanen, K., & Kristensson, P. O. (2014) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2555691 · Cited by 62
Analysis and prevention of AI-based phishing email attacks
Eze, C. S., & Shamir, L. (2024) · Electronics · doi:10.3390/electronics13101839 · Cited by 47
Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges
Huang, B., Chen, C., & Shu, K. (2025) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3715073.3715076 · Cited by 42
“This holistic approach not only improves the attribution process but also fosters the development of innovative methodologies that are better adapted to the evolving nature of digital content.”
LLM-PBE: Assessing Data Privacy in Large Language Models
Li, Q., Hong, J., Xie, C., Tan, J., Xin, R., Hou, J., Yin, X., Wang, Z., Hendrycks, D., Wang, Z., et al. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3681954.3681994 · Cited by 42
Privacy Implications of Retrieval-Based Language Models
Huang, Y., Gupta, S., Zhong, Z., Li, K., & Chen, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.921 · Cited by 25
Preferential Attachment in Online Networks: Measurement and Explanations
Kunegis, J., Blattner, M., & Moser, C. (2013) · arXiv · doi:10.48550/arxiv.1303.6271 · Cited by 14
External Data Extraction Attacks Against Retrieval-Augmented Large Language Models
He, Y., Chen, Y., Li, Y., Shao, S., Qi, L., Li, B., Tao, D., & Qin, Z. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3705326 · Cited by 4
A Large-Scale Corpus of E-mail Conversations with Standard and Two-Level Dialogue Act Annotations
Taniguchi, M., Ueda, Y., Taniguchi, T., & Ohkuma, T. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.436 · Cited by 3
Metadata
- Authors
- Klimt, B., & Yang, Y.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2004
- Pages
- 217-226
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1603920809
- Cited by
- 1,157
- References
- 0
- FWCI
- 15.40
- 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
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.