Dataset Name: Spam Email Dataset

Description: This dataset contains a collection of email text messages, labeled as either spam or not spam. Each email message is associated with a binary label, where "1" indicates that the email is spam, and "0" indicates that it is not spam. The dataset is intended for use in training and evaluating spam email classification models.

Columns:

text (Text): This column contains the text content of the email messages. It includes the body of the emails along with any associated subject lines or headers.

spam_or_not (Binary): This column contains binary labels to indicate whether an email is spam or not. "1" represents spam, while "0" represents not spam.

Usage: This dataset can be used for various Natural Language Processing (NLP) tasks, such as text classification and spam detection. Researchers and data scientists can train and evaluate machine learning models using this dataset to build effective spam email filters.

Dataset

This dataset was created by Dibyajit dhara

Contents

Email Thread Summary Dataset

Overview:

The Email Thread Dataset consists of two main files: email_thread_details and email_thread_summaries. These files collectively offer a comprehensive compilation of email thread information alongside human-generated summaries.

Email Thread Details:

Description:

The email_thread_details file provides a detailed perspective on individual email threads, encompassing crucial information such as subject, timestamp, sender, recipients, and the content of the email.

Columns:

• thread_id: A unique identifier for each email thread.
• subject: Subject of the email thread.
• timestamp: Timestamp indicating when the message was sent.
• from: Sender of the email.
• to: List of recipients of the email.
• body: Content of the email message.

Additional Information:

The "to" column is available in both CSV and Pickle (pkl) formats, facilitating convenient access to recipient information as a column of lists of strings.

Email Thread Summaries:

Description:

The email_thread_summaries file contains concise summaries crafted by human annotators for each email thread, offering a high-level overview of the content.

Columns:

• thread_id: A unique identifier for each email thread.
• summary: A concise summary of the email thread.

Dataset Structure:

The dataset is organized into threads and emails. There are a total of 4,167 threads and 21,684 emails, providing a rich source of information for analysis and research purposes.

• Threads: 4,167 threads
• Emails: 21,684 emails

Language:

• Languages: English (en)

Use Cases:

1. Natural Language Processing (NLP) Research:
   • Analyze email thread contents and human-generated summaries for advancements in NLP tasks.
2. Text Summarization Models:
   • Train and evaluate text summarization models using the provided email threads and summaries.
3. Email Analytics:
   • Gain insights into communication patterns, sender-receiver relationships, and content analysis.

File Formats:

• CSV Files:
  • Easily importable into various data analysis tools.
• Pickle (pkl) Files:
  • Facilitates direct reading of the "to" column as a column of lists of strings.

• JSON Files:

  • Offers compatibility with JSON data structures, providing an additional option for users who prefer or require this widely-used format in their analytical workflows.

  • ****JSON File Features Description****

    [
      {
        "thread_id": [unique identifier],
        "subject": "[email thread subject]",
        "timestamp": [timestamp in milliseconds],
        "from": "[sender's name and identifier]",
        "to": [
          "[recipient 1]",
          "[recipient 2]",
          "[recipient 3]",
          ...
        ],
        "body": "[email content]"
      },
      ...
    ]
    

    [
      {
        "thread_id": [unique identifier],
        "summary": "[summary content]"
      },
      ...
    ]
    

****Files Structure:****

- Dataset
 ├── CSV
 │  ├── email_thread_details.csv
 │  └── email_thread_summaries.csv
 ├── Pickle
 │  ├── email_thread_details.pkl
 │  └── email_thread_summaries.pkl
 └── JSON
   ├── email_thread_details.json
   └── email_thread_summaries.json

License:

This dataset is provided under the MIT License.

Disclaimer:

The dataset has been anonymized and sanitized to ensure privacy and confidentiality.

Overview

This is a temporal hypergraph dataset, which here means a sequence of timestamped hyperedges where each hyperedge is a set of nodes. In email communication, messages can be sent to multiple recipients. In this dataset, nodes are email addresses at Enron, and a hyperedge is comprised of the sender and all recipients of the email. Only email addresses from a core set of employees are included. Timestamps are in ISO8601 format.

This dataset was collected and prepared by the CALO Project (A Cognitive Assistant that Learns and Organizes). It contains data from about 150 users, mostly senior management of Enron, organized into folders. The corpus contains a total of about 0.5M messages. This data was originally made public and posted to the web by the Federal Energy Regulatory Commission during its investigation.

The email dataset was later purchased by Leslie Kaelbling at MIT and turned out to have a number of integrity problems. A number of folks at SRI, notably Melinda Gervasio, worked hard to correct these problems, and it is thanks to them that the dataset is available. The dataset here does not include attachments, and some messages have been deleted "as part of a redaction effort due to requests from affected employees". Invalid email addresses were converted to something of the form user@enron.com whenever possible (i.e., the recipient is specified in some parseable format like "Doe, John" or "Mary K. Smith") and to no_address@enron.com when no recipient was specified.

Statistics

Some basic statistics of this dataset are:

• number of nodes: 148
• number of timestamped hyperedges: 10,885
• distribution of the connected components:

Component Size, Number

• 143, 1
• 1, 5

Source of original data

Source: email-Enron dataset

References

If you use this dataset, please cite these references:

• Simplicial closure and higher-order link prediction. Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.
• Enron Email Dataset, William Cohen, 2015.

Phishing Email Dataset

This dataset on Hugging Face is a direct copy of the 'Phishing Email Detection' dataset from Kaggle, shared under the GNU Lesser General Public License 3.0. The dataset was originally created by the user 'Cyber Cop' on Kaggle. For complete details, including licensing and usage information, please visit the original Kaggle page.

1) Data Introduction • The Email Phishing Dataset is designed for phishing email detection using machine learning.

2) Data Utilization (1) Email Phishing Dataset has characteristics that: • All emails were refined and subjected to a custom NLP feature extraction pipeline focused on phishing metrics. • This dataset contains no raw text or headers, only features engineered for model training/testing. (2) Email Phishing Dataset can be used to: • Developing an email detection model: It can be used to train and evaluate AI models that classify normal mail and phishing mail using various characteristics such as email body, subject, and sender. • E-mail security policy and threat analysis research: Analyzing real phishing cases and normal email data to derive the characteristics of phishing attacks, and use them to establish effective email security policies and develop threat response strategies.

To quote the data source: "This dataset was collected and prepared by the CALO Project (A Cognitive Assistant that Learns and Organizes). It contains data from about 150 users, mostly senior management of Enron, organized into folders. The corpus contains a total of about 0.5M messages. This data was originally made public, and posted to the web, by the Federal Energy Regulatory Commission during its investigation. The email dataset was later purchased by Leslie Kaelbling at MIT, and turned out to have a number of integrity problems. A number of folks at SRI, notably Melinda Gervasio, worked hard to correct these problems, and it is thanks to them (not me) that the dataset is available. The dataset here does not include attachments, and some messages have been deleted "as part of a redaction effort due to requests from affected employees". Invalid email addresses were converted to something of the form user@enron.com whenever possible (i.e., recipient is specified in som

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This is a collection of text data from 160 emails. For each email, we have included the subject, text, and type of phishing email. The four types of emails included in the dataset are fraud, false positives (legitimate emails), phishing, and commercial spam. 40 of each type of email are in the dataset. This type of data can be used to help build a more complex email spam blocker and could have applications in cybersecurity.

We have curated 7 repositories.The Ling and Enron datasets possess just two features: ‘Subject’ and ‘Body’. The other datasets consists of six features, namely ‘Sender’, ‘Receiver’, ‘Date’, ‘Subject’, ‘Body’, and ‘Urls’.Please cite this dataset:A. I. Champa, M. F. Rabbi, and M. F. Zibran, “Curated datasets and feature analysis for phishing email detection with machine learning,” in 3rd IEEE International Conference on Computing and Machine Intelligence (ICMI), 2024, pp. 1–7 (to appear).or @inproceedings{champa2024curated, title={Curated Datasets and Feature Analysis for Phishing Email Detection with Machine Learning}, author={Champa, Arifa I and Rabbi, Md Fazle and Zibran, Minhaz F}, booktitle={3rd IEEE International Conference on Computing and Machine Intelligence (ICMI)}, pages = {1--7 (to appear)}, year={2024}}

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Email Spam Classification, Text Classification Dataset

The dataset consists of a collection of emails categorized into two major classes: spam and not spam. It is designed to facilitate the development and evaluation of spam detection or email filtering systems.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on roman@kucev.com to buy the dataset

The spam emails in the dataset are typically unsolicited and unwanted messages that aim to promote products or services, spread malware, or deceive recipients for various malicious purposes. These emails often contain misleading subject lines, excessive use of advertisements, unauthorized links, or attempts to collect personal information.

The non-spam emails in the dataset are genuine and legitimate messages sent by individuals or organizations. They may include personal or professional communication, newsletters, transaction receipts, or any other non-malicious content.

The dataset encompasses emails of varying lengths, languages, and writing styles, reflecting the inherent heterogeneity of email communication. This diversity aids in training algorithms that can generalize well to different types of emails, making them robust against different spammer tactics and variations in non-spam email content.

The dataset's possible applications:

• spam detection
• fraud detection
• email filtering systems
• customer support automation
• natural language processing

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💴 Buy the Dataset: This is just an example of the data. Leave a request on roman@kucev.com to discuss your requirements, learn about the price and buy the dataset.

File with the extension .csv

includes the following information:

• title: title of the email,
• text: text of the email,
• type: type of the email

Email spam might be collected in accordance with your requirements.

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