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The task of Chinese text spam detection is very challenging due to both glyphand phonetic variations of Chinese characters. This paper proposes a novelframework to jointly model Chinese variational, semantic, and contextualizedrepresentations for Chinese text spam detection task. In particular, aVariation Family-enhanced Graph Embedding (VFGE) algorithm is designed based ona Chinese character variation graph. The VFGE can learn both the graphembeddings of the Chinese characters (local) and the latent variation families(global). Furthermore, an enhanced bidirectional language model, with acombination gate function and an aggregation learning function, is proposed tointegrate the graph and text information while capturing the sequentialinformation. Extensive experiments have been conducted on both SMS and reviewdatasets, to show the proposed method outperforms a series of state-of-the-artmodels for Chinese spam detection.

Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation