FredZhang7/all-scam-spam

This is a large corpus of 42,619 preprocessed text messages and emails sent by humans in 43 languages. is_spam=1 means spam and is_spam=0 means ham.

1040 rows of balanced data, consisting of casual conversations and scam emails in ≈10 languages, were manually collected and annotated by me, with some help from ChatGPT.

Some preprcoessing algorithms

• spam_assassin.js, followed by spam_assassin.py
• enron_spam.py

Data composition

Description

To make the text format between sms messages and emails consistent, email subjects and content are separated by two newlines:

text = email.subject + "\n\n" + email.content

Suggestions

• If you plan to train a model based on this dataset alone, I recommend adding some rows with is_toxic=0 from FredZhang7/toxi-text-3M. Make sure the rows aren't spam.

Other Sources

• https://huggingface.co/datasets/sms_spam
• https://github.com/MWiechmann/enron_spam_data
• https://github.com/stdlib-js/datasets-spam-assassin
• https://repository.ortolang.fr/api/content/comere/v3.3/cmr-simuligne.html