A comprehensive framework for predicting public opinion by tracking multi-informational dynamics

Graphical abstract

References

[1] Gao Y, Wang X, He X, Feng H, Zhang Y. Rumor detection with self-supervised learning on texts and social graph. Frontiers of Computer Science, 2023, 17( 4): 174611

[2] Zhang M, Zheng R, Chen J, Zhu J, Liu R, Sun S, Wu Q. Emotional component analysis and forecast public opinion on micro-blog posts based on maximum entropy model. Cluster Computing, 2019, 22( 3): 6295–6304

[3] Rahman S, Jahan N, Sadia F, Mahmud I. Social crisis detection using twitter based text mining-a machine learning approach. Bulletin of Electrical Engineering and Informatics, 2023, 12( 2): 1069–1077

[4] Wu Z, Dai X, Xia R. Pairwise tagging framework for end-to-end emotion-cause pair extraction. Frontiers of Computer Science, 2023, 17( 2): 172314

[5] Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation. The Journal of Machine Learning Research, 2003, 3: 993–1022

[6] Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V. RoBERTa: a robustly optimized BERT pretraining approach. 2019, arXiv preprint arXiv: 1907.11692

[7] Olivares K G, Challu C, Marcjasz G, Weron R, Dubrawski A. Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx. International Journal of Forecasting, 2023, 39( 2): 884–900

[8] Zhang Y, Feng Y. Two-layer coupled network model for topic derivation in public opinion propagation. China Communications, 2020, 17( 3): 176–187

[9] Vaish A, Grossmann T, Woodward A. Not all emotions are created equal: the negativity bias in social-emotional development. Psychological Bulletin, 2008, 134( 3): 383–403

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