Multitask Learning Neural Networks for Pandemic Prediction with Public Stance Enhancement
34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022
; 2022-October:1262-1270, 2022.
Article
in English
| Scopus | ID: covidwho-2320881
ABSTRACT
State and local governments have imposed health policies to contain the spread of COVID-19 since it had a serious impact on human daily life. However, the public stance on these measures may be time-varying. It is likely to escalate the infection in the area where the public is negative or resistant. To take advantage of the correlation between public stance on health policies and the COVID-19 statistics, we propose a novel framework, Multitask Learning Neural Networks for Pandemic Prediction with Public Stance Enhancement (MP3), which is composed of three modules (1) Stance awareness module to make stance detection on health policies from users' tweets in social media and convert them into a stance time series. (2) Temporal feature extraction module that applies Convolution Neural Network and Recurrent Neural Network to extract and fuse local patterns and long-term correlations from COVID-19 statistics. Moreover, a Stance Latency-aware Attention is proposed to capture dynamic social effects and fuse them with temporal features. (3) Multi-task prediction module to adopt Graph Convolution Network to model the spread of pandemic and employ multi-task learning to simultaneously predict COVID-19 statistics and the trend of public stance on health policies. The proposed framework outperforms state-of-the-art baselines on both confirmed cases and deaths prediction tasks. © 2022 IEEE.
epidemic prediction; social media mining; stance detection; time series; Convolution; Convolutional neural networks; Forecasting; Learning systems; Recurrent neural networks; Social networking (online); Health policy; Learning neural networks; Local government; Multitask learning; Social media minings; State governments; Temporal features; Times series; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022
Year:
2022
Document Type:
Article
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