The paper titled Peeking Strategy for Online News Diffusion Prediction via Machine Learning is accepted by Physica A.
For computational social scientists, cascade size prediction and fake news detection are two primary problems in news diffusion or computational mass communication research. Previous studies predict news diffusion via peeking the social process (temporal structure) data in the initial stage, which is summarized as Peeking strategy. However, the combination of peeking strategies and machine learning algorithms has not been fully investigated. To predict cascade size and detect false news, we adopt Peeking strategy based on well-known machine learning algorithms. Our results show that Peeking strategy can effectively improve the accuracy of cascade size prediction. Meanwhile, we can peek into a smaller time window to achieve a high performance in predicting the cascade size compared with previous methods. Nevertheless, we find that Peeking strategy with network structures fails in significantly improving the performance of false news detection. Finally, we argue that cascade structure properties can aid in prediction of cascade size, but not for the false news detection.
Keywords: News Diffusion, Tree-like Network, Peeking strategy, Cascade Structure
Zhang, Yaotian., Shang, Keke., Feng, Mingming., Ran, Yijun., Wang, Cheng-Jun * (2022) Peeking Strategy for Online News Diffusion Prediction Via Machine Learning. Accepted by Physica A. Preprint available at SSRN: https://ssrn.com/abstract=4025009 or http://dx.doi.org/10.2139/ssrn.4025009
The 2021-2022 Journal’s Impact IF of Physica A: Statistical Mechanics and its Applications is 3.263, ranking it 28 out of 85 in Physics, Multidisciplinary.