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.
Similar content being viewed by others
Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Ale L, Zhang N, Wu H, Chen D, Han T (2019) Online proactive caching in mobile edge computing using bidirectional deep recurrent neural network. IEEE Internet Things J 6(3):5520–5530
An J, Cho S (2015) Variational autoencoder based anomaly detection using reconstruction probability. Spec Lect IE 2(1):1–18
Cao Q, Shen H, Cen K, Ouyang W, Cheng X (2017) Deephawkes: bridging the gap between prediction and understanding of information cascades. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1149–1158
Cao Q, Shen H, Gao J, Wei B, Cheng X (2020) Popularity prediction on social platforms with coupled graph neural networks. In: Proceedings of the 13th international conference on web search and data mining, pp 70–78
Che Z, Chang J, Zhang H, Du F (2021) A microblog popularity prediction model based on temporal sequence features and text features. In: 2021 IEEE international conference on computer science, electronic information engineering and intelligent control technology (CEI). IEEE, pp 795–800
Chen G, Kong Q, Xu N, Mao W (2019) NPP: a neural popularity prediction model for social media content. Neurocomputing 333:221–230
Cheng J, Adamic L, Dow PA, Kleinberg JM, Leskovec J (2014) Can cascades be predicted? In: Proceedings of the 23rd international conference on world wide web, pp 925–936
Chen X, Zhou F, Zhang K, Trajcevski G, Zhong T, Zhang F (2019) Information diffusion prediction via recurrent cascades convolution. In: 2019 IEEE 35th international conference on data engineering (ICDE). IEEE, pp 770–781
Chkhartishvili A, Gubanov D (2019) On approaches to identifying information spread channels in online social networks. In: 2019 Twelfth international conference “Management of large-scale system development” (MLSD). IEEE, pp 1–4
Cui P, ** S, Yu L, Wang F, Zhu W, Yang S (2013) Cascading outbreak prediction in networks: a data-driven approach. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 901–909
Gao X, Cao Z, Li S, Yao B, Chen G, Tang S (2019) Taxonomy and evaluation for microblog popularity prediction. ACM Trans Knowl Discov Data (TKDD) 13(2):1–40
Gao J, Zhang S, Zhao L, Shen X (2020) The design of dynamic probabilistic caching with time-varying content popularity. IEEE Trans Mob Comput 20(4):1672–1684
Gao S, Ma J, Chen Z (2014) Effective and effortless features for popularity prediction in microblogging network. In: Proceedings of the 23rd international conference on World Wide Web, pp 269–270
Garg N, Sellathurai M, Bhatia V, Bharath B, Ratnarajah T (2019) Online content popularity prediction and learning in wireless edge caching. IEEE Trans Commun 68(2):1087–1100
Hajiakhondi-Meybodi Z, Mohammadi A, Abouei J, Plataniotis KN (2023) CLSA: contrastive learning-based survival analysis for popularity prediction in MEC networks. ar**v preprint ar**v:2303.12097
HajiAkhondi-Meybodi Z, Mohammadi A, Hou M, Rahimian E, Heidarian S, Abouei J, Plataniotis KN (2022) Multi-content time-series popularity prediction with multiple-model transformers in MEC networks. ar**v preprint ar**v:2210.05874
Hu Z, Fang C, Wang Z, Tseng S-M, Dong M (2023) Many-objective optimization based-content popularity prediction for cache-assisted cloud-edge-end collaborative IoT networks. IEEE Internet Things J
Kingma DP, Welling M (2013) Auto-encoding variational bayes. ar**v preprint ar**v:1312.6114
Kong S, Mei Q, Feng L, Ye F, Zhao Z (2014) Predicting bursts and popularity of hashtags in real-time. In: Proceedings of the 37th international ACM SIGIR conference on research and development in information retrieval, pp 927–930
Kong Q, Rizoiu M-A, **e L (2020) Modeling information cascades with self-exciting processes via generalized epidemic models. In: Proceedings of the 13th international conference on web search and data mining, pp 286–294
Lee JG, Moon S, Salamatian K (2012) Modeling and predicting the popularity of online contents with Cox proportional hazard regression model. Neurocomputing 76(1):134–145
Liao D, Xu J, Li G, Huang W, Liu W, Li J (2019) Popularity prediction on online articles with deep fusion of temporal process and content features. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 200–207
Li C, Ma J, Guo X, Mei Q (2017) Deepcas: an end-to-end predictor of information cascades. In: Proceedings of the 26th international conference on World Wide Web, pp 577–586
Liu C, Wang W, Sun Y (2019) Community structure enhanced cascade prediction. Neurocomputing 359:276–284
Ma Z, Sun A, Cong G (2013) On predicting the popularity of newly emerging hashtags in t witter. J Am Soc Inf Sci Technol 64(7):1399–1410
Qian Y, Xu W, Liu X, Ling H, Jiang Y, Chai Y, Liu Y (2022) Popularity prediction for marketer-generated content: a text-guided attention neural network for multi-modal feature fusion. Inf Process Manag 59(4):102984
Romero D, Tan C, Ugander J (2013) On the interplay between social and topical structure. In: Proceedings of the international AAAI conference on web and social media, vol 7, pp 516–525
Shen H, Wang D, Song C, Barabási A-L (2014) Modeling and predicting popularity dynamics via reinforced poisson processes. In: Proceedings of the AAAI conference on artificial intelligence, vol 28, pp 1–13
Shulman B, Sharma A, Cosley D (2016) Predictability of popularity: gaps between prediction and understanding. In: Proceedings of the international AAAI conference on web and social media, vol 10, pp 348–357
Sinha S, Dieng AB (2021) Consistency regularization for variational auto-encoders. Adv Neural Inf Process Syst 34:12943–12954
Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In: 2010 IEEE Second International Conference on Social Computing, pp. 177–184 (2010). IEEE
Tan J, Liu W, Wang T, Zhao M, Liu A, Zhang S (2021) A high-accurate content popularity prediction computational modeling for mobile edge computing using matrix completion technology. Trans Emerg Telecommun Technol 32(6):3871
Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. ar**v preprint ar**v:1710.10903
Wang H, Li Y, Feng Z, Feng L (2013) Retweeting analysis and prediction in microblogs: an epidemic inspired approach. China Commun 10(3):13–24
Wang J, Zheng VW, Liu Z, Chang KC-C (2017) Topological recurrent neural network for diffusion prediction. In: 2017 IEEE international conference on data mining (ICDM). IEEE, pp 475–484
Wu Z, Zhou J, Liu L, Li C, Gu F (2022) Deep popularity prediction in multi-source cascade with HERI-GCN. In: 2022 IEEE 38th international conference on data engineering (ICDE). IEEE Computer Society, pp 1714–1726
Yu L, Cui P, Wang F, Song C, Yang S (2015) From micro to macro: uncovering and predicting information cascading process with behavioral dynamics. In: 2015 IEEE international conference on data mining. IEEE, pp 559–568
Zadeh AH, Sharda R (2014) Modeling brand post popularity dynamics in online social networks. Decis Support Syst 65:59–68
Zadeh A, Sharda R (2022) How can our tweets go viral? Point-process modelling of brand content. Inf Manag 59(2):103594
Zhang Z, Yin Z, Wen J, Sun L, Su S, Philip SY (2021) Deepblue: Bi-layered lSTM for tweet popularity estimation. IEEE Trans Knowl Data Eng 34(10):4737–4752
Zhang X, Zou Y, Shi W (2017) Dilated convolution neural network with LeakyReLU for environmental sound classification. In: 2017 22nd international conference on digital signal processing (DSP). IEEE, pp 1–5
Zhao T, Lin J, Zhang Z (2022) Case-based reasoning and attribute features mining for posting-popularity prediction: a case study in the online automobile community. Mathematics 10(16):2868
Zhao Q, Erdogdu MA, He HY, Rajaraman A, Leskovec J (2015) Seismic: a self-exciting point process model for predicting tweet popularity. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1513–1522
Zhou F, Xu X, Trajcevski G, Zhang K (2021) A survey of information cascade analysis: models, predictions, and recent advances. ACM Comput Surv (CSUR) 54(2):1–36
Zhou F, Xu X, Zhang K, Trajcevski G, Zhong T (2020) Variational information diffusion for probabilistic cascades prediction. In: IEEE INFOCOM 2020-IEEE conference on computer communications. IEEE, pp 1618–1627
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).
Author information
Authors and Affiliations
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.
Corresponding author
Ethics declarations
Conflict of interest
The authors have no competing interests to declare that are relevant to the content of this article.
Ethical approval
This article does not contain any studies with animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-023-01165-x