Bayesian Based Comment Spam Defending Tool

TL;DR

This paper presents a Bayesian algorithm-based software tool designed to detect and prevent comment spam in blogs by calculating the probability of spam based on comment content, effectively reducing unwanted comments and bandwidth usage.

Contribution

The paper introduces a novel Bayesian spam filtering tool specifically for blog comments, demonstrating its effectiveness through experimental results.

Findings

The Bayesian tool accurately identifies spam comments.

The tool reduces bandwidth consumption caused by spam.

Experimental results confirm the tool's effectiveness.

Abstract

Spam messes up user's inbox, consumes network resources and spread worms and viruses. Spam is flooding of unsolicited, unwanted e mail. Spam in blogs is called blog spam or comment spam.It is done by posting comments or flooding spams to the services such as blogs, forums,news,email archives and guestbooks. Blog spams generally appears on guestbooks or comment pages where spammers fill a comment box with spam words. In addition to wasting user's time with unwanted comments, spam also consumes a lot of bandwidth. In this paper, we propose a software tool to prevent such blog spams by using Bayesian Algorithm based technique. It is derived from Bayes' Theorem. It gives an output which has a probability that any comment is spam, given that it has certain words in it. With using our past entries and a comment entry, this value is obtained and compared with a threshold value to find if it exceeds the threshold value or not. By using this concept, we developed a software tool to block comment spam. The experimental results show that the Bayesian based tool is working well. This paper has the major findings and their significance of blog spam filter.