Spam is an inseparable part of the Internet. Alas, this “contagion” is unbeatable in principle, the only way to fight spam is to filter it.
Internet companies fight against spam for multiple reasons. Firstly, spam emails clog up users’ inboxes and cause frustration, which can lead to a negative perception of the company in question. Secondly, spam can also contain harmful malware or phishing schemes that can compromise a user’s personal information or computer system.
Furthermore, large amounts of spam traffic to a website can negatively affect server performance and increase costs for internet service providers. In addition to these practical reasons, fighting against spam is also important from an ethical standpoint as it promotes safety and privacy for users online.
By using filtering technologies, machine learning algorithms and other methods, internet companies aim to reduce the amount of spam that reaches their users and maintain a positive online experience for them.
Machine learning algorithms can detect spam in texts through a combination of techniques like Natural Language Processing (NLP), pattern recognition, and statistical analysis.
First, the algorithm will analyze large sets of data to learn patterns, such as keywords or phrases commonly used in spam messages. Then, the algorithm might use NLP to identify certain words that tend to signal spam or appear frequently in spam text messages.
Statistical analysis is also used to determine which texts may be too similar to others previously classified as spam. Once trained with enough data, machine learning algorithms can accurately predict whether a new message is likely to be spam or not.
This technology helps reduce the amount of unwanted communications we receive daily while improving email security by detecting phishing scams before they infiltrate users’ mailboxes.
I have written some anti-spam models in Python. At the link below I am sharing with you the code for one of them.
