Journal: Frontiers in Neurorobotics

Article Title: Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

doi: 10.3389/fnbot.2021.674322

Figure Lengend Snippet: The overall architecture of CasWarn. (A) : Three perspectives of information cascade. (a-1): The overall information cascade after time slice; (a-2): the composition form of dissemination scale feature after time slice; (a-3): features in the user's view, including emotional polarity ratio and semantic evolution features. (B) : Different feature preprocessing and embedding representation processes. (C) : The end-to-end neural network model. It first fuses the quantitative and emotional features through the CNN-E1 layer and then embeds the temporal semantic evolution features through the Bi-LSTM model, it next uses the CNN-E2 model to fuse the three features again. Finally, the FC-softmax layer predicts the result.

Article Snippet: Next, we concatenate the semantic evolution feature f 2 ( h c s e f t ) with the output feature f 1 ( h c ) of the previous layer: As shown in the CNN-E2 layer in , the concatenated data are fused again by the CNN feature fusion layer to learn the potential relationship between different features: Then, f 3 ( h des ) is followed by a fully connected (FC-softmax layer) logistic classification layer: The vector h ( c i ) ∈ R 2 can be regarded as the last feature representation in the model, which will be used to predict the virality of the cascade.

Techniques: