Web objectionable text content detection using topic modeling technique

Web 2.0 technologies have made it easily for Web users to create and spread objectionable text content, which has been shown harmful to Web users, especially young children. Although detection methods based on key word list are superior in achieving faster detection and lower memory consumption, they fail to detect text content that is objectionable in semantic description. A framework that can perfectly integrate semantic model and detection method is proposed to perform probability inference for detecting this kind of Web text content. Based on the observation that an objectionable scene could be described by a set of sentences, a topic model which is learnt from the set is employed to act as a semantic model of the objectionable scene. For a given sentence, probability value which shows the likelihood of the sentence with respect to the model is calculated in the framework. Then we use a mapping function to transform the probability value into a new indicator which is convenient for making final decision. Extensive comparison experiments on two real world text sets show that the framework can effectively recognize semantic objectionable text, and both the detection rate and the false alarm rate are superior to those of traditional methods.