Spam Filters, AI, and Email [Infographic]


Phishing emails and types of other invasive emails have filled inboxing in the wake of COVID-19. Around 18 million emails were automatically filtered by Gmail from the start of the pandemic to today, says Panda Security. There are a number of manual strategies to manage unwanted emails, but none as effective as one that uses a machine learning framework to filter junk mail.

AI is commonly used within outbound email marketing, but less commonly used for filtering unwanted inbound emails. In email hosts’ current structure, filter rules can be determined manually once an email has infiltrated an inbox. The machine-learning algorithm then places emails it deems to be similar to the same fate, spam.

The time, storage, and the cost of spam mail are in the billions. It seems advantageous, then, to create a structure without the possibility of false positives using stronger AI technologies. The categorical clustering algorithm aims to do just that, and its dataset accounts for over 200,000 junk emails.

On an even more basic level, Medium explains how to create these types of filters from scratch. The publication details exploratory data analysis, data preprocessing, feature extraction, scoring and metrics, embedding and neutral network for improvements, and deep learning.

By simply exploring and understanding the data and visualizing that data to improve intuition you can effectively design this filter for your own inbox. Text cleaning, feature extraction, and the metrics will then adequately ready your host for proper filtering.

While we wait for this technology to be commercialized and distributed with big data from the most widely used hosts, use these 5 tactics to stop spam mail and manage your inbox.





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