Introducing the Enron Corpus

By Bryan Klimt, Yiming Yang. 2004

783 citations recorded by the source corpus.

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Citations in the corpus

The security of machine learning (2010, methodology): “We use the Text Retrieval Conference (TREC) 2005 spam corpus (Cormack and Lynam 2005), which is based on the Enron email corpus (Klimt and Yang 2004) and contains 92,189 emails (52,790 spam and 39,399 ham).”
Communication Networks from the Enron Email Corpus “It's Always About the People. Enron is no Different” (2005, background): “Separately, Shetty and Adibi (a) and Klimt and Yang (2004a) cleaned the corpus, explored the distribution of messages across users and time, and characterized discussion threads (Klimt and Yang, 2004b).”
Introducing the Webb Spam Corpus: Using Email Spam to Identify Web Spam Automatically (2006, methodology): “Similar to email spam research on the experimental evaluation of spam filters using large spam corpora such as the Enron [15] and SpamArchive [20] corpora, future Web spam research depends on the availability of large collections of Web spam data.”
Information Retrieval for E-Discovery (2013, background): “” By far the best known such collection was produced as a collaboration between MIT, SRI and CMU to support a joint research project involving e-mail analysis [83].”
Statistical properties of community structure in large social and information networks (2008, unknown): “99 Enron email dataset [99] Answers 488,484 1,240,189 0.”
Modeling Latent Biographic Attributes in Conversational Genres (2009, methodology): “In order to empirically support this motivation, we also tested the performance of the models explored in this paper on the Enron email corpus (Klimt and Yang, 2004).”
Machine Learning in the Presence of an Adversary: Attacking and Defending the SpamBayes Spam Filter (2008, methodology): “For my experiments, I use the Text Retrieval Conference (TREC) 2005 spam conference corpus [3], which is based on the Enron dataset [11].”
LifeLogging: Personal Big Data (2014, background): “this tends to have more success for office environments where we log digital activities (web usage, emails, document accesses) rather than physical ones, there are examples of recent work with healthcare workers in clinical practice who have to log their work and record their clinical notes at the e”
Behavior of Machine Learning Algorithms in Adversarial Environments (2010, methodology): “In these experiments, I use the Text Retrieval Conference (TREC) 2005 spam corpus as described by Cormack and Lynam [2005], which is based on the Enron email corpus [Klimt and Yang, 2004] and contains 92, 189 emails (52, 790 spam and 39, 399 ham).”
Bringing science to digital forensics with standardized forensic corpora (2009, methodology): “For example, the Enron Email Dataset (Klimt and Yang, 2004) is a corpus of 619,446 real email messages from the 158 users inside the Enron Corporation.”
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters (2008, unknown): “99 Enron email dataset [100] Answers 488,484 1,240,189 0.”
Long short term memory neural network for keyboard gesture decoding (2015, methodology): “The third dataset was synthetically generated from the Enron dataset [20].”
A Comprehensive Survey on Community Detection With Deep Learning (2021, methodology): “To discover communities in time-varying networks [111]–[113], a framework called semi-supervised evolutionary autoencoder (sE-Autoencoder) [78] was developed within an evolutionary clustering framework.”
Spam Corpus Creation for TREC (2005, methodology): “Construction of a gold standard for the Enron Corpus, and the tools to facilitate that construction, remains a work in progress.”
DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks (2020, methodology): “In Enron, the communication links denote email interactions between core employees, while the links in UCI represent messages sent between peer users on an online social network platform.”
Analyzing Leakage of Personally Identifiable Information in Language Models (2023, background): “• Enron [31] consists of corporate e-mails by employees that were placed into the public domain after the Enron scandal.”