A semantic-aided abnormal broadcast content analysis method.

Abnormal broadcast activities are becoming more and more rampant, since it is easy and cheap to achieve a radio broadcasting station. These abnormal broadcasts disseminate illegal information, which can have serious negative impacts on society. However, detecting and tracing these abnormal broadcast contents can be challenging as they are often scattered and difficult to identify. In this article, we propose a semantic-aided analysis method for broadcast content (SAAM), which is based on Latent Dirichlet Allocation (LDA) to construct a model for detecting and analyzing abnormal broadcasts. The LDA model intelligently captures the latent semantic topics implied in the broadcasting content, enabling the establishment of criteria for identifying anomaly and facilitating to investigate. The analysis method further exploits the Apriori algorithm to explore the associations between the content of abnormal broadcast and relevant organizations based on semantic topics from LDA. This allows for detailed analysis of abnormal broadcasts as well as tracing relevant organizations involved in such activities. In the experiment, the abnormal broadcast data is collected to verify the accuracy of SAAM in detecting and tracing abnormal broadcast. The experiment results show that, the accuracy rate of SAAM in detecting abnormal broadcasts is more than 0.91 and the tracing-accuracy is more than 0.8.