Adversarial Spam Detector With Character Similarity Network

Abstract

More and more social platforms suffer from the spam messages and trouble in detecting them. Lizhi, one of the most famous audio APPs in China, also suffers from the spam messages. The spam detector in Lizhi faces two major challenges: adversarial actions taken by the spammers and lack of labeled data. In this article, we propose a novel adversarial spam detector based on character similarity network for detecting adversarial spam messages in Lizhi, namely Lizhi adversarial spam detector. The character similarity network is designed to solve the adversarial actions before being taken by the spammers, and the character embedding model is proposed to learn the embeddings of all the characters in the corpus. Then, the model generates the sentence embedding of the messages sent by users. At last the classifier will predict whether the message is a spam. Also, the model utilizes active learning to solve the problem of lack of labeled data. Both of offline and online experiments are conducted to confirm the effectiveness of the proposed method.