Detecting Nasty Comments from BBS Posts
Year: 2010
Type: conference-paper
Abstract: We propose a method to detect Japanese nasty comments from posts on bulletin board systems (BBS). Nasty comments can cause many social problem, because they express potentially harmful words and phrases. There are methods to recognize harmful words, but they are insufficient. Therefore, we present a method for detecting such comments on a BBS with many posts using an n-gram model. In addition, we compared our method with a support vector machine (SVM) that is based on nasty words. As a result, we detected nasty comments that are different to those by the SVM. We also observe higher detection accuracy by combining two methods.
Authors: Tatsuya Ishisaka, Kazuhide Yamamoto
Institution: Nagaoka University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W791836620