Design and Application of an AI-Based Text Content Moderation System

Abstract

Cloud computing, 5G mobile network, and other new technologies have been applied in higher education in recent years. The education video resource service system with adaptive multiterminals has received widespread attention from the field. From these videos, students can get new knowledge and use the system’s built-in text comment function to communicate and interact with others. However, due to the fast increase in the number of such text comments, the traditional text content moderation methods such as the keyword method and the regular expression method can no longer meet the growing business needs. Therefore, to solve this matter, this study designed a text content moderation (TCM) system based on artificial intelligence (AI), which uses artificial intelligence and cloud-based algorithm models to analyse and recognize the text comments submitted from the web-end and app-end of the education video resource service system and completes operations such as automatic detection and manual moderation. The proposed TCM system can significantly improve the efficiency of text content moderation.

Compensating Removed Frequency Components: Thwarting Voice Spectrum Reduction Attacks

“…The ACE is evaluated based on word/character recognition, which is widely adopted by most content moderation systems that use keyword filtering [31] and regular expressions [33]. However, some content moderation systems may utilize speech understanding methods that consider the text semantics instead of individual words [63]. We are unable to evaluate the ACE performance with the speech understanding model due to the lack of a dataset.…”