a day ago
- Bayesian content-based filtering is an effective method for stopping spam, using statistical analysis of word probabilities.
- Statistical approaches outperform rule-based filters by calculating true probabilities, reducing false positives which are more harmful than missed spam.
- The algorithm calculates spam probability for each word based on occurrences in spam and nonspam corpora, then combines the top 15 most interesting words.
- Personalized filtering per user is recommended, using their own mail to build word probability tables, making filters harder for spammers to evade.
- Bayesian filters automatically adapt to spam evolution, such as new word variants, without requiring manual rule updates.
- Effective filtering can reduce spam's economic viability by lowering response rates, potentially discouraging spammers from sending it.