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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… See the full description on the dataset page: https://huggingface.co/datasets/FredZhang7/all-scam-spam.

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
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Dataset Card

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.

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Some preprcoessing algorithms

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

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Data composition

Spam vs Non-spam (Ham)

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Description

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

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

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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.

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Other Sources

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