C-RBFNN: A user retweet behavior prediction method for hotspot topics based on improved RBF neural network

Liu, Yanbing; Zhao, Jinzhe; Xiao, Yunpeng

doi:10.1016/j.neucom.2017.09.015

Cited by 53 publications

(21 citation statements)

References 8 publications

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“…Recently, [57] constructed a user retweet behavior prediction model based on RBF (radical basis function) neural network. They also introduced another model called C-RBF (cloudbased RBF) using fuzzy which could incorporate the uncertainty in a user's behavior.…”

Section: A Current Research Areas Under Information Diffusion 1)mentioning

confidence: 99%

A Review of Critical Research Areas under Information Diffusion in Social Networks

Kakar¹,

Mehrotra²

2020

IJACSA

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An online social network is a network where people exchange their ideas or opinions. Exchange of ideas between users leads to spread of information at a larger scale in the social networks. This spread of information is also called information diffusion. This work is dedicated to identifying research areas under the umbrella of Information Diffusion. The objective of this work is to present an extensive review of such areas, identify the existing research gaps and explore future directions of work. The review also identifies the methodologies, features and aspects studied in the current literature and proposes the optimal feature set to improve performance. This review will enable researchers to quickly identify the research areas, the current gaps and steer them into the possible future directions associated with them.

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Section: A Current Research Areas Under Information Diffusion 1)mentioning

confidence: 99%

A Review of Critical Research Areas under Information Diffusion in Social Networks

Kakar¹,

Mehrotra²

2020

IJACSA

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“…In case of Twitter, a large body of literature address the problem of retweet prediction and user influence detection [18], [19]. Liu et al [20] proposed a user behavior model for retweet prediction. Recently, studies on the role of multimodality in retweet prediction have gained much focus [21], [22].…”

Section: E Performance Of Guvecmentioning

confidence: 99%

Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory

Dutta

Das

Chakraborty

2019

2019 IEEE International Conference on Data Mining (ICDM)

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Online discussions are valuable resources to study user behaviour on a diverse set of topics. Unlike previous studies which model a discussion in a static manner, in the present study, we model it as a time-varying process and solve two interrelated problems -predict which user groups will get engaged with an ongoing discussion, and forecast the growth rate of a discussion in terms of the number of comments. We propose RGNet (Relativistic Gravitational Network), a novel algorithm that uses Einstein Field Equations of gravity to model online discussions as 'cloud of dust' hovering over a user spacetime manifold, attracting users of different groups at different rates over time. We also propose GUVec, a global user embedding method for an online discussion, which is used by RGNet to predict temporal user engagement. RGNet leverages different textual and network-based features to learn the dust distribution for discussions.We employ four baselines -first two using LSTM architecture, third one using Newtonian model of gravity, and fourth one using a logistic regression adopted from a previous work on engagement prediction. Experiments on Reddit dataset show that RGNet achieves 0.72 Micro F1 score and 6.01% average error for temporal engagement prediction of user groups and growth rate forecasting, respectively, outperforming all the baselines significantly. We further employ RGNet to predict non-temporal engagement -whether users will comment to a given post or not. RGNet achieves 0.62 AUC for this task, outperforming existing baseline by 8.77% AUC. Post User cluster 1 User cluster 2 User cluster 3 User cluster 4 Comments Post • Communicative Proximity: If u i , u j replied to each other, then increment A ij by 2.

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“…The other approach is the machine learning method based on feature engineering. Liu et al [11] proposed a retweeting behavior prediction model based on fuzzy theory and neural network algorithm, which can effectively predict the user retweeting behavior and dynamically perceive the changes in hotspot topics. This research method relies on the knowledge of domain experts, and the process of feature selection may take a long time.…”

Section: Introductionmentioning

confidence: 99%

UserRBPM: User Retweet Behavior Prediction with Graph Representation Learning

Guo

Liu

2021

Wireless Communications and Mobile Computing

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Social and information networks such as Facebook, Twitter, and Weibo have become the main social platforms for the public to share and exchange information, where we can easily access friends’ activities and in turn be influenced by them. Consequently, the analysis and modeling of user retweet behavior prediction have an important application value, such as information dissemination, public opinion monitoring, and product recommendation. Most of the existing solutions for user retweeting behavior prediction are usually based on network topology maps of information dissemination or designing various handcrafted rules to extract user-specific and network-specific features. However, these methods are very complex or heavily dependent on the knowledge of domain experts. Inspired by the successful use of neural networks in representation learning, we design a framework, UserRBPM, to explore potential driving factors and predictable signals in user retweet behavior. We use the graph embedding technology to extract the structural attributes of the ego network, consider the drivers of social influence from the spatial and temporal levels, and use graph convolutional networks and the graph attention mechanism to learn its potential social representation and predictive signals. Experimental results show that our proposed UserRBPM framework can significantly improve prediction performance and express social influence better than traditional feature engineering-based approaches.

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C-RBFNN: A user retweet behavior prediction method for hotspot topics based on improved RBF neural network

Cited by 53 publications

References 8 publications

A Review of Critical Research Areas under Information Diffusion in Social Networks

A Review of Critical Research Areas under Information Diffusion in Social Networks

Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory

UserRBPM: User Retweet Behavior Prediction with Graph Representation Learning

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