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Continually, one of the most the fragile states in Thailand are originated from cyber-bullying across social media networks (OSNs). Cyber-bullying intentionally is plotted to offend other people, particularly in politics. This paper develops a novel linguistic model to detect the Thai-bullying label on OSNs. Our model is based on “Gated Recurrent Unit (GRU)” that has a pre-process for dimensional reduction algorithm called “Lexical Thai Grammatical Filtering (TGF)”. Our developed TGF-GRU is formulated by the 10,900 Thai texts from posts/comments on Facebook. From the results, TGF can improve the accuracy of normal GRU as 8.41% with a little time consumption. Notwithstanding, some synonyms, homographs or insinuations of Thai jargons can easily confuse the detection. In a nutshell, TGF-GRU model will be able to be used as an additional AI feature to autonomously detect the inappropriate Thai text before a user posts or comments on Facebook. For years, some cyber-bullying labels (e.g. pejorative, sexual comment, hate speech, rumor, slandering, etc.) will have been autonomously detected and filtered out; the causes of social fragile state will be gradually mitigated by Thai-bullying detector.
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