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Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature
Liqun Gao1, Yujia Liu1, Hongwu Zhuang1
1Software Engineering Center, College of Computer, National University of Defense Technology, ChangSha, China.
Frontiers in Neurorobotics
|June 14, 2021
Summary
CasWarn, a new deep learning model, predicts public opinion cascade virality effectively without network structure. It uses key features like dissemination scale and semantic evolution for timely and accurate early warnings.
Area of Science:
- Artificial Intelligence
- Social Network Analysis
- Computational Social Science
Background:
- Public opinion early warning systems increasingly utilize agent technology and deep learning for efficiency.
- Predicting information cascade virality is crucial for public opinion analysis, with existing methods often relying on network structure.
- Recent approaches explore network-agnostic virality prediction but lack comprehensive feature integration.
Purpose of the Study:
- To propose CasWarn, an innovative cascade virality prediction model for intelligent agents.
- To enhance the automatic analysis and prediction of public opinion information cascades.
- To develop a model effective across different industries without requiring underlying network structure.
Main Methods:
- CasWarn extracts key network-agnostic features: dissemination scale, emotional polarity ratio, and semantic evolution.
- Two improved neural network frameworks are employed to embed these extracted features.
- A classification task is applied to predict the cascade virality based on embedded features.
Main Results:
- Comprehensive experiments on two large social network datasets demonstrate CasWarn's effectiveness.
- CasWarn achieves timely and accurate cascade virality predictions.
- Each feature model within CasWarn contributes positively to overall performance.
Conclusions:
- CasWarn offers a robust solution for predicting public opinion cascade virality, deployable in intelligent agents.
- The model's network-agnostic approach broadens its applicability.
- Feature engineering, including dissemination scale, emotional polarity, and semantic evolution, is vital for accurate virality prediction.
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