A method of extracting malicious expressions in bulletin board systems by using context analysis

Bulletin board systems are well-known basic services on the Internet for information frequent exchange. The convenience of bulletin boards enables us to communicate with other persons and to read the communication contents at any time. However, malicious postings about crimes are serious problems for serving companies and users. The extracting scheme of the traditional methods depends on words or a sequence of words without considering contexts of articles and, therefore, it takes a lot of human efforts to alert malicious articles. In order to reduce the human efforts, this paper presents a new filtering algorithm that can recover the error rate of false positive for non-malicious articles by using context analysis. The presented scheme builds detecting knowledge by introducing multi-attribute rules. By the experimental results for 11,019 test data, it turns out that sensitivity and specificity of the presented method become 38.7 and 24.1 (%) points higher than traditional method, respectively.