On modeling and predicting popularity dynamics via integrating generative model and rich features

Feng, Xiaodong; Zhao, Qihang; Ma, Jie; Jiang, Guoyin

doi:10.1016/j.knosys.2020.105786

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…PETM ( Gao, Ma & Chen, 2015 ) expanded upon the foundation of the RPP model through a temporal mapping process. Feng et al (2020) proposed a feature-regularized reinforced Poisson process (FRRPP), which leveraged feature regression terms to capture the correlations between different posts. The second important method in generative approaches is the Hawkes process.…”

Section: Related Workmentioning

confidence: 99%

Structural-topic aware deep neural networks for information cascade prediction

Zhou,

Feng,

Feng

2024

PeerJ Computer Science

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It is critical to accurately predict the future popularity of information cascades for many related applications, such as online opinion warning or academic influence evaluation. Despite many efforts devoted to developing effective prediction approaches, especially the recent presence of deep learning-based model, the structural information of the cascade network is ignored. Thus, to make use of the structural information in cascade prediction task, we propose a structural-topic aware deep neural networks (STDNN), which firstly learns the structure topic distribution of each node in the cascade, feeds it to a sequential neural network, and finally predicts the future popularity of the cascades. It can inherit the high interpretability of Hawkes process and possesses the high predictive power of deep learning methods, bridging the gap between prediction and understanding of information cascades by capturing indicative graph structures. We evaluate our model through quantitative experiments, where our model exhibits promising performance, efficiency higher than the baselines.

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Section: Related Workmentioning

confidence: 99%

Structural-topic aware deep neural networks for information cascade prediction

Zhou,

Feng,

Feng

2024

PeerJ Computer Science

Self Cite

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“…Besides, in order to decrease the algorithm computation complexity as well, the same combinations generated from different cascades can be merged and an average critical penalty margin is calculated for the combinations. For example, the combination (1, 3, 4) appears in the two cascades c 1 = (1, 5, 3, 7, 4) and c 2 = (1, 3, 5, 7, 4) and the critical penalty margins of (1,3,4) in the two cascades can be calculated as C 1 3,4 = 0.74 and C 2 3,4 = 1.32, then the average critical penalty margin of the combination (1, 3, 4) is c = 1.03 and the combination should be considered once in an epoch. Here only the dominant combination is selected for learning.…”

Section: Dominant Combinationmentioning

confidence: 99%

“…The diffusion of information on social networks is a complex and dynamic process, which aroused the great research enthusiasm of researchers. The researches on information diffusion play an important role in many fields such as predicting how popular a piece of information will become [1,2,3,4], finding some nodes in a social network that could maximize the spread of influence [5,6,7], how much a cascade will grow [8,9] and so on. In this paper, we study the task of information diffusion prediction.…”

Section: Introductionmentioning

confidence: 99%

Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks

Xie,

Wang,

Jia

2021

Preprint

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The prediction for information diffusion on social networks has great practical significance in marketing and public opinion control. Cascade prediction aims to predict the individuals who will potentially repost the message on the social network. One kind of methods either exploit demographical, structural, and temporal features for prediction, or explicitly rely on particular information diffusion models. The other kind of models are fully data-driven and do not require a global network structure. Thus massive diffusion prediction models based on network embedding are proposed. These models embed the users into the latent space using their cascade information, but are lack of consideration for the intervene among users when embedding. In this paper, we propose an independent asymmetric embedding method to learn social embedding for cascade prediction. Different from existing methods, our method embeds each individual into one latent influence space and multiple latent susceptibility spaces. Furthermore, our method captures the co-occurrence regulation of user combination in cascades to improve the calculating effectiveness. The results of extensive experiments conducted on real-world datasets verify both the predictive accuracy and costeffectiveness of our approach.

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“…1) The static and dynamic metrics Some scholars had shown that the number of themes and mentioned peoples have positive impacts on the event [27], while out-links reduces these impacts [28]. Agarwal showed that words and comments of the influential microblog have more lasting impact than other microblogs [29].…”

Section: B the Event Attention Degreementioning

confidence: 99%

Tracking Attention of Social Media Event by Hidden Markov Model–Cases from Sina Weibo

Ma¹,

Jiao²,

Song³

2021

IEEE Access

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Sina Weibo has significantly impact on the information diffusion processes in many realworld social events. A large number of active users on Sina Weibo not only push the opinion diffusion, but also increase the influence abilities of events which conversely attracted much attentions of users to follow them. How to effectively track the event attention of users is one of the most important channels to get the public opinions. In order to predict the event attention more accurately, motivated by observations of social events' influence concerning with users and microblogs, we quantify the user popularity from the four dimensions: the user activity, the user behavior, the user authenticity and the user infection ability. And the non-collinearity of these four dimensions is tested to ensure the comprehensiveness and nonredundancy of the evaluation. Then, combining with the logic framework of Hidden Markov Model, we propose an algorithm to predict the Weibo event attention by using the user popularity. Meanwhile, in order to better detect the performance of the prediction algorithm, we integrate the static and dynamic information of microblog content to directly quantify the current Weibo event attention as a benchmark, and the performance of four prediction algorithms (including our algorithm) is tested with six real data sets which are chosen from the popular events in China from 2019 to 2020. Through comparison, we find that the user popularity can be used to predict the event attention, and the Hidden Markov Model prediction method by using the user popularity shows good prediction performance.

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On modeling and predicting popularity dynamics via integrating generative model and rich features

Cited by 8 publications

References 26 publications

Structural-topic aware deep neural networks for information cascade prediction

Structural-topic aware deep neural networks for information cascade prediction

Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks

Tracking Attention of Social Media Event by Hidden Markov Model–Cases from Sina Weibo

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