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 This data was originally made public, and posted to the web, by the Federal Energy Regulatory Commission during its investigation.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

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 This data was originally made public and posted to the web by the Federal Energy Regulatory Commission during its investigation.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.

This dataset contains the data from roughly two years of operating PrivacyMail.info, an Open Source Email privacy measurement platform. It contains slightly over 500.000 commercial newsletters, as crowdsourced by users of PrivacyMail.info. You can find the methodology discussed in our paper: Max Maass, Stephan Schwär, and Matthias Hollick. "Towards transparency in email tracking." Annual Privacy Forum, 2019. The source code can be found on github.com/privacymail/privacymail

Please note that, due to its crowdsourced nature, this dataset is a sample of opportunity - it is not representative for all newsletters on the Internet, and likely contains biases based on how it was collected. Notably, German-language newsletters will likely be heavily over-represented.

Dataset Structure
The dataset is structured as follows: On the top level are folders describing the website the newsletter belongs to. Inside that folder are subfolders for each identity that was registered for that website. Inside each of these folders are a series of .eml files that represent the received email messages.

Copyright and Licensing
This dataset is set to non-public due to copyright concerns: The contents of the email messages are (presumably) protected by copyright in most jurisdictions. Most copyright doctrines contain exceptions for non-commercial research use - thus, we feel it is appropriate and acceptable to share the data on a case-by-case basis, the same way we did before shutting down PrivacyMail.info. When requesting access to the data, please briefly describe what research you want to conduct with it, and we will grant you access.

We thus do not put any explicit license on this dataset. Please do not share the raw data publicly. We request that you cite the above-mentioned paper and this dataset in any publications that result from it.

This dataset consists of a curated and anonymized collection of real job application confirmation emails from a Gmail inbox. It includes confirmation emails, rejection notices, and other relevant correspondences. The dataset was originally curated to address the challenge of eliminating manual job application tracking, allowing for automatic tracking directly from the inbox, capturing application confirmations and rejection notifications.

The dataset has been carefully pre-processed, cleaned, and enriched with derived features such as:

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4. 📩 Email Subject and Body
5. 🏢 Company Name (Parsed from Subject/Body)
6. 📊 Application Status Insights

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⚠️ Disclaimer: All personal identifiable information (PII) such as names and email addresses have been fully anonymized or redacted. This dataset is intended strictly for educational and research purposes. All personally identifiable information (PII) has been carefully anonymized. Any personal names found in the dataset have been replaced with the fictional name "Michael Gary Scott" as a placeholder. This character reference is used purely for fun and does not correspond to any real individual. Please ensure any further use of this dataset respects privacy and ethical data handling practices.

This dataset contains anonymised survey responses from a comprehensive study conducted to explore current email management practices among users. The survey aimed to gain insights into how individuals handle and organize their email communications in various contexts. The survey questionnaire consisted of carefully designed questions related to email usage patterns, organisational strategies, folder structures, and automation utilised for email management. The survey also explored participants' preferences for automated rule-based filtering functionality and any challenges they face in effectively managing their mailbox.

Researchers and professionals interested in email management and information organisation can leverage this dataset for research, analysis, and potential improvements in email client design and functionality.

We kindly request that any publications or research utilising this dataset appropriately acknowledge and cite the original source to ensure proper attribution to the survey and its participants.

The Enron email dataset contains approximately 500,000 emails generated by employees of the Enron Corporation. It was obtained by the Federal Energy Regulatory Commission during its investigation of Enron's collapse.

This is the May 7, 2015 Version of dataset, as published at https://www.cs.cmu.edu/~./enron/

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EnronSR, is a benchmark dataset based on the Enron email corpus that contains both naturally occurring human- and AI-generated email replies for the same set of messages. This resource enables the public benchmarking of novel language-generation models and facilitates a comparison against the strong, production-level baseline of Google Smart Reply used by millions of people.

The Panza Emails dataset

This dataset contains collections of emails of three authentic users (david, isabel, and marcus), with personal information (names, places, etc.) replaced by other ones for donor privacy. Except for these changes, the language of the emails is genuine. The intention of this dataset is to allow researchers to study strategies for text personalization. The data was donated explicitly for this purpose. This dataset is ethically collected and fully licensed for… See the full description on the dataset page: https://huggingface.co/datasets/ISTA-DASLab/Panza-emails.

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MultiSocial is a dataset (described in a paper) for multilingual (22 languages) machine-generated text detection benchmark in social-media domain (5 platforms). It contains 472,097 texts, of which about 58k are human-written and approximately the same amount is generated by each of 7 multilingual large language models by using 3 iterations of paraphrasing. The dataset has been anonymized to minimize amount of sensitive data by hiding email addresses, usernames, and phone numbers.

If you use this dataset in any publication, project, tool or in any other form, please, cite the a paper.

Disclaimer

Due to data source (described below), the dataset may contain harmful, disinformation, or offensive content. Based on a multilingual toxicity detector, about 8% of the text samples are probably toxic (from 5% in WhatsApp to 10% in Twitter). Although we have used data sources of older date (lower probability to include machine-generated texts), the labeling (of human-written text) might not be 100% accurate. The anonymization procedure might not successfully hiden all the sensitive/personal content; thus, use the data cautiously (if feeling affected by such content, report the found issues in this regard to dpo[at]kinit.sk). The intended use if for non-commercial research purpose only.

Data Source

The human-written part consists of a pseudo-randomly selected subset of social media posts from 6 publicly available datasets:

1. Telegram data originated in Pushshift Telegram, containing 317M messages (Baumgartner et al., 2020). It contains messages from 27k+ channels. The collection started with a set of right-wing extremist and cryptocurrency channels (about 300 in total) and was expanded based on occurrence of forwarded messages from other channels. In the end, it thus contains a wide variety of topics and societal movements reflecting the data collection time.

2. Twitter data originated in CLEF2022-CheckThat! Task 1, containing 34k tweets on COVID-19 and politics (Nakov et al., 2022, combined with Sentiment140, containing 1.6M tweets on various topics (Go et al., 2009).

3. Gab data originated in the dataset containing 22M posts from Gab social network. The authors of the dataset (Zannettou et al., 2018) found out that “Gab is predominantly used for the dissemination and discussion of news and world events, and that it attracts alt-right users, conspiracy theorists, and other trolls.” They also found out that hate speech is much more prevalent there compared to Twitter, but lower than 4chan's Politically Incorrect board.

4. Discord data originated in Discord-Data, containing 51M messages. This is a long-context, anonymized, clean, multi-turn and single-turn conversational dataset based on Discord data scraped from a large variety of servers, big and small. According to the dataset authors, it contains around 0.1% of potentially toxic comments (based on the applied heuristic/classifier).

5. WhatsApp data originated in whatsapp-public-groups, containing 300k messages (Garimella & Tyson, 2018). The public dataset contains the anonymised data, collected for around 5 months from around 178 groups. Original messages were made available to us on request to dataset authors for research purposes.

From these datasets, we have pseudo-randomly sampled up to 1300 texts (up to 300 for test split and the remaining up to 1000 for train split if available) for each of the selected 22 languages (using a combination of automated approaches to detect the language) and platform. This process resulted in 61,592 human-written texts, which were further filtered out based on occurrence of some characters or their length, resulting in about 58k human-written texts.

The machine-generated part contains texts generated by 7 LLMs (Aya-101, Gemini-1.0-pro, GPT-3.5-Turbo-0125, Mistral-7B-Instruct-v0.2, opt-iml-max-30b, v5-Eagle-7B-HF, vicuna-13b). All these models were self-hosted except for GPT and Gemini, where we used the publicly available APIs. We generated the texts using 3 paraphrases of the original human-written data and then preprocessed the generated texts (filtered out cases when the generation obviously failed).

The dataset has the following fields:

• 'text' - a text sample,

• 'label' - 0 for human-written text, 1 for machine-generated text,

• 'multi_label' - a string representing a large language model that generated the text or the string "human" representing a human-written text,

• 'split' - a string identifying train or test split of the dataset for the purpose of training and evaluation respectively,

• 'language' - the ISO 639-1 language code identifying the detected language of the given text,

• 'length' - word count of the given text,

• 'source' - a string identifying the source dataset / platform of the given text,

• 'potential_noise' - 0 for text without identified noise, 1 for text with potential noise.

ToDo Statistics (under construction)

Forager.ai's Small Business Contact Data set is a comprehensive collection of over 695M professional profiles. With an unmatched 2x/month refresh rate, we ensure the most current and dynamic data in the industry today. We deliver this data via JSONL flat-files or PostgreSQL database delivery, capturing publicly available information on each profile.

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Amazon-Fraud is a multi-relational graph dataset built upon the Amazon review dataset, which can be used in evaluating graph-based node classification, fraud detection, and anomaly detection models.

Dataset Statistics

Graph Construction

The Amazon dataset includes product reviews under the Musical Instruments category. Similar to this paper, we label users with more than 80% helpful votes as benign entities and users with less than 20% helpful votes as fraudulent entities. we conduct a fraudulent user detection task on the Amazon-Fraud dataset, which is a binary classification task. We take 25 handcrafted features from this paper as the raw node features for Amazon-Fraud. We take users as nodes in the graph and design three relations: 1) U-P-U: it connects users reviewing at least one same product; 2) U-S-V: it connects users having at least one same star rating within one week; 3) U-V-U: it connects users with top 5% mutual review text similarities (measured by TF-IDF) among all users.

To download the dataset, please visit this Github repo. For any other questions, please email ytongdou(AT)gmail.com for inquiry.

Introduction

The dataset comprises over 10,000 chat conversations, each focusing on specific Telecom related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.

Topic Diversity

The chat dataset covers a wide range of conversations on Telecom topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various Telecom use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.

Language Variety & Nuances

The conversations in this dataset capture the diverse language styles and expressions prevalent in Swedish Telecom interactions. This diversity ensures the dataset accurately represents the language used by Swedish speakers in Telecom contexts.

The dataset encompasses a wide array of language elements, including:

This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to Swedish Telecom interactions.

Conversational Flow and Interaction Types

The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of Telecom customer-agent interactions.

Each of these conversations contains various aspects of conversation flow like: