Deep learning for classifying thai deceptive messages

Panida Songram, Suchart Khummanee, Phatthanaphong Chomphuwiset, Chatklaw Jareanpon, Laor Boongasame, Khanabhorn Kawattikul


Online deception has become a major problem affecting people, society, the economy, and national security. It is mostly done by spreading deceptive messages because message are quickly spread on social networks and are easily accessed by anyone. Detecting deceptive messages is challenging as the messages are unstructured, informal, and complex; this extends into Thai language messages. In this paper, various deep learning models are proposed to detect deceptive messages under two feature extraction trials. A balanced two-class dataset of deceptive and truthful Thai messages (n=2378) is collected from Facebook pages. Instance features are encoded using word embeddings (Thai2Fit) and one-hot encoding techniques. Five classification models, convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent units (BiGRU), CNN-BiLSTM, and CNN-BiGRU, are proposed and evaluated upon the dataset with each feature extraction technique. The experimental results show that all the proposed models had excellent accuracy (95.59% to 98.74%) and BiLSTM with one-hot encoding gave the best performance, achieving 98.74% accuracy.


Deceptive messages; Deep learning; Online deception; Social networks; Text classification; Thai deception

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