CAS2VEC: Network-Agnostic Cascade Prediction in Online Social Networks

Kefato, Zekarias T.; Sheikh, Nasrullah; Bahri, Leila; Soliman, Amira; Montresor, Alberto; Girdzijauskas, Šarūnas

doi:10.1109/snams.2018.8554730

Cited by 19 publications

(11 citation statements)

References 20 publications

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“…They use large-scale cascade indicators for intelligent early warning, and the main focus is predicted performance indicators and timeliness indicators (early detection). In order to obtain the macro-level predictive value of information cascade in social networks in time, many works decided to use machine learning models and divide the scale of cascade into two types of tasks: One uses regression, like (Kefato et al, 2018;Zhu et al, 2018), to predict the potential scale of an information cascade, and the other uses the classification models, such as Zhao et al (2015), to define the form of dissemination as viral or non-viral. Based on the machine learning models, most of the work focuses on the following information cascade features: (a) network topology (e.g., user relationship, first-order relationship network structure of user, etc.…”

Section: Related Workmentioning

confidence: 99%

“…As mentioned, recent models in this field apply a neural network framework like CNN (Kefato et al, 2018), RNN (Li et al, 2017), and GNN (Chen et al, 2019), etc. By using the above information cascade features, the neural network model's excellent feature extraction ability is efficient for agent systems.…”

Section: Related Workmentioning

confidence: 99%

“…We process it into a sequence by extracting the number of forwardings in the time slice, as shown in Figure 1a-2. Specifically, by extracting the forwarding times in the time slice, similar to Kefato et al (2018), the sequence of integers representing the number of events included in each slice becomes:…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Information cascade virality means that some information may be widely spread in the network in a short time. It may be organized, planned behavior, or the extension of controversial social emergencies (Kefato et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Subbian et al (2017) propose an agnostic network-based method to reduce the network structure information in the information cascade process. Kefato et al (2018) apply deep learning models to agnostic-network virus prediction, use the number of forwarding in the time sequence process as the feature, and use the CNN model to predict whether the information will explode. However, the construction model lacks critical features strongly related to the information cascade.…”

Section: Introductionmentioning

confidence: 99%

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Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

et al. 2021

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With the rapid popularity of agent technology, a public opinion early warning agent has attracted wide attention. Furthermore, a deep learning model can make the agent more automatic and efficient. Therefore, for the agency of a public opinion early warning task, the deep learning model is very suitable for completing tasks such as popularity prediction or emergency outbreak. In this context, improving the ability to automatically analyze and predict the virality of information cascades is one of the tasks that deep learning model approaches address. However, most of the existing studies sought to address this task by analyzing cascade underlying network structure. Recent studies proposed cascade virality prediction for agnostic-networks (without network structure), but did not consider the fusion of more effective features. In this paper, we propose an innovative cascade virus prediction model named CasWarn. It can be quickly deployed in intelligent agents to effectively predict the virality of public opinion information for different industries. Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

et al. 2021

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Information Cascading in Social Networks

Gao

Zhou

Jia

et al. 2021

MDATA: A New Knowledge Representation Model

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Modeling the social influence of COVID-19 via personalized propagation with deep learning

Liu

Cao

et al. 2022

World Wide Web

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Social influence prediction has permeated many domains, including marketing, behavior prediction, recommendation systems, and more. However, traditional methods of predicting social influence not only require domain expertise, they also rely on extracting user features, which can be very tedious. Additionally, graph convolutional networks (GCNs), which deals with graph data in non-Euclidean space, are not directly applicable to Euclidean space. To overcome these problems, we extended DeepInf such that it can predict the social influence of COVID-19 via the transition probability of the page rank domain. Furthermore, our implementation gives rise to a deep learning-based personalized propagation algorithm, called DeepPP. The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis. Four social networks from different domains as well as two COVID-19 datasets were used to analyze the proposed algorithm’s efficiency and effectiveness. Compared to other baseline methods, DeepPP provides more accurate social influence predictions. Further, experiments demonstrate that DeepPP can be applied to real-world prediction data for COVID-19.

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CAS2VEC: Network-Agnostic Cascade Prediction in Online Social Networks

Cited by 19 publications

References 20 publications

Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

Information Cascading in Social Networks

Modeling the social influence of COVID-19 via personalized propagation with deep learning

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