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VGCas: distinguishing the cascade structure and the global structure in popularity prediction

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Abstract

Online social platforms like Twitter, Weibo, and Facebook have developed rapidly in recent years. These platforms offer people more opportunities to exchange information. Understanding and predicting information cascade on social media platforms is a fundamental problem and one of the primary challenges is to predict the popularity of information. However, most existing methods fail to distinguish the cascade structural feature and global structural feature, resulting in unsatisfactory prediction performance. In this paper, we propose a novel framework named VGCas to distinguish the features of cascade structure and global structure and combine them with temporal features of the cascade to predict popularity. To extract the cascade structural feature and global structural feature simultaneously, we utilize a graph attention based variational autoencoder. Then, we use a gated recurrent unit to extract the temporal feature from the time series. Finally, we feed the combination of the two outputs into a multilayer perceptron to predict popularity. We verify the effectiveness of VGCas by applying it to predict retweet cascades on Twitter and Sina Weibo. Experimental results demonstrate a substantial improvement in predictive accuracy over existing approaches.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by the Key R &D and Transformation Plan of Qinghai Province (No. 2022-QY-218), and the National Natural Science Foundation of China (62102262).

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Authors and Affiliations

  1. College of International Business, Zhejiang Yuexiu University, Shaoxing, 312000, Zhejiang, China

    Wei Yu & Xiaoming Li

  2. Law School, Tianjin University, Tianjin, 300072, Tianjin, China

    Xue Chen

  3. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, Tianjin, China

    Jun Wang & Ying Sun

  4. School of Mining and Coal, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China

    Ying Sun

  5. School of Computer Science, Qinghai Minzu University, Xining, 810007, Qinghai, China

    Minghu Tang

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  1. Wei Yu
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  2. Xue Chen
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  3. Xiaoming Li
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  4. Jun Wang
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  5. Ying Sun
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  6. Minghu Tang
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Contributions

WY: Mainly responsible for data collating and writing. XL: Getting data and the results verified. XC and JW: Mainly modified and polished the article. YS and MT: Primarily designed the thesis program.

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Correspondence to Xiaoming Li.

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Yu, W., Chen, X., Li, X. et al. VGCas: distinguishing the cascade structure and the global structure in popularity prediction. Soc. Netw. Anal. Min. 14, 2 (2024). https://doi.org/10.1007/s13278-023-01165-x

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