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A semantic-aided abnormal broadcast content analysis method
Qi Zhang1, Fengming Zhou1, Yuzhi Zhang1
1School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang, 621010, China.
Scientific Reports
|June 23, 2026
Summary
This study introduces a semantic-aided analysis method (SAAM) for detecting and tracing abnormal broadcasts. SAAM utilizes Latent Dirichlet Allocation (LDA) and the Apriori algorithm to achieve high accuracy in identifying and investigating illegal broadcast content.
Area of Science:
- Information Science
- Computer Science
- Signal Processing
Background:
- Abnormal broadcast activities are increasing due to low costs.
- These broadcasts spread illegal information, causing societal harm.
- Detecting and tracing scattered abnormal broadcast content is challenging.
Purpose of the Study:
- To propose a novel semantic-aided analysis method (SAAM) for detecting and analyzing abnormal broadcasts.
- To leverage Latent Dirichlet Allocation (LDA) for topic modeling and anomaly detection.
- To utilize the Apriori algorithm for tracing organizations involved in abnormal broadcasts.
Main Methods:
- Developed a SAAM based on Latent Dirichlet Allocation (LDA) for topic modeling.
- Employed the Apriori algorithm to discover associations between broadcast content and organizations.
- Collected abnormal broadcast data for experimental validation.
Main Results:
- SAAM achieved an accuracy rate of over 0.91 in detecting abnormal broadcasts.
- The tracing accuracy of SAAM exceeded 0.8.
- The method effectively identifies anomaly criteria and facilitates investigation.
Conclusions:
- SAAM provides an effective approach for detecting and tracing abnormal broadcasts.
- The integration of LDA and Apriori enhances the analysis of broadcast content and associated organizations.
- The proposed method demonstrates high accuracy and practical applicability.
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