Community conflict prediction method based on spliced BiLSTM

Si Chen; Xiaodong Cai; Bo Li; Zhenzhen Hou

doi:10.1117/12.2540244

27 November 2019 Community conflict prediction method based on spliced BiLSTM

Si Chen, Xiaodong Cai, Bo Li, Zhenzhen Hou

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113211V (2019) https://doi.org/10.1117/12.2540244
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

Abstract

Existing community conflict prediction models usually use a single unidirectional LSTM network to process graph and word embeddings simultaneously. However，there is no temporal coherence between graph and word embeddings. And their importance for prediction is different. A community conflict prediction method based on spliced bidirectional LSTM is proposed. Firstly, two bidirectional LSTMs are utilized to process graph and word embeddings respectively to break temporal dependency. Secondly, the hidden states of the two bidirectional LSTMs are weighted. Finally, the weighted hidden states are spliced and fed into subsequent layers of the neural network to predict conflicts. Experimental results show that this method can improve the AUC value to 0.733 on the Reddit dataset, and reduce the number of iterations of training.

Citation Download Citation

Si Chen, Xiaodong Cai, Bo Li, and Zhenzhen Hou "Community conflict prediction method based on spliced BiLSTM", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113211V (27 November 2019); https://doi.org/10.1117/12.2540244

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