  The task of Chinese text spam detection is very challenging due to both glyph
and phonetic variations of Chinese characters. This paper proposes a novel
framework to jointly model Chinese variational, semantic, and contextualized
representations for Chinese text spam detection task. In particular, a
Variation Family-enhanced Graph Embedding (VFGE) algorithm is designed based on
a Chinese character variation graph. The VFGE can learn both the graph
embeddings of the Chinese characters (local) and the latent variation families
(global). Furthermore, an enhanced bidirectional language model, with a
combination gate function and an aggregation learning function, is proposed to
integrate the graph and text information while capturing the sequential
information. Extensive experiments have been conducted on both SMS and review
datasets, to show the proposed method outperforms a series of state-of-the-art
models for Chinese spam detection.
