An emotion role mining approach based on multiview ensemble learning in social networks

Du, Yajun; Wang, Yakun; Hu, Jinrong; Li, Xianyong; Chen, Xiaoliang

doi:10.1016/j.inffus.2022.07.010

Cited by 22 publications

(5 citation statements)

References 37 publications

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“…Optimum 𝛼 and 𝒘 values may be estimated from a set of labeled samples 𝒔 , 𝑦 𝑛 1 … 𝑁 by minimizing the cost function (2).…”

Section: Graph Regularized Alpha Integrationmentioning

confidence: 99%

“…This broad research area has been named in different ways, for instance, sensor data fusion, decision fusion, multimodal fusion, heterogeneous sensor fusion, mixture of experts, classifier combination, and multiway signal processing. The applications of fusion methods cover a large number of interesting problems including emotion recognition, fall detection, daily activity recognition, and hand movement recognition [1]; social networks [2]; Alzheimer's disease [3]; automatic sleep staging [4]; image object classification [5]; multi-media [6]; brain computer interfacing [7]; archaeological ceramic provenance [8]; object detection fusing infrared and visible images [9]; banking customer classification [10]; stock movement prediction [11]; maritime tracking [12]; and several benchmark datasets [13] [14].…”

Section: Introductionmentioning

confidence: 99%

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Graph Regularization Methods in Soft Detector Fusion

Salazar,

Safont,

Vergara

et al. 2023

IEEE Access

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This paper presents a theoretical derivation of two new graph-based regularization methods for fusing the individual results of multiple detectors (two-class classifiers). The proposed approach considers linear combination of the individual detector statistics and its extension to a general nonlinear fusion method known as α-integration. A cost function that includes a mean-square error and a regularization term is minimized. The inclusion of the regularization term, which is based on graph signal processing, reduces the dispersion of the fused statistics, and thus improves the separation between the fused statistics corresponding to every detection hypothesis. The proposed methods (linear and non-linear regularized α-integration) are experimentally compared with commonly used classification methods (random forest, linear and quadratic discriminant analysis, and naive Bayes) and competitive fusion methods (Dempster-Shafer, copulas, behavior knowledge space, independent component analysis mixture modeling, majority voting, the mean, and αintegration). Two challenging problems were approached using simulated and electroencephalographic data, respectively: (i) detection of ultrasound pulses buried in high noise, and (ii) detection of changes in electroencephalographic signals for neuropsychological test staging. An experimental convergence analysis of the proposed regularized method for these two applications is included. Besides, the proposed methods were tested using several benchmark datasets. Results on the basis of classification accuracy, kappa index, F1 score, and receiver operating characteristic curve analysis demonstrate the superiority of the proposed regularized fusion methods.

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“…Optimum 𝛼 and 𝒘 values may be estimated from a set of labeled samples 𝒔 , 𝑦 𝑛 1 … 𝑁 by minimizing the cost function (2).…”

Section: Graph Regularized Alpha Integrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph Regularization Methods in Soft Detector Fusion

Salazar,

Safont,

Vergara

et al. 2023

IEEE Access

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“…Neighbor-based Improved the case for either positive or negative in the discrete approach [18,19] Network-based Expanded the scale of emotional propagation abilities to portray complex social phenomena. [22,[50][51][52][53] (1) Discrete opinion models divide emotions into several classes. The Voter model [14] is the simplest method: individuals randomly adopt the opinion of one neighbor.…”

Section: Continuousmentioning

confidence: 99%

“…Xiong et al [22] proposed an emotional independent cascade model wherein an individual's emotion can affect their friends continuously in the subsequent diffusion process. Du et al [52] attempted to expand emotion to the role-detection task based on emotional contagion methods. Liang and Duan [53] proposed a three-way decision framework to investigate competitive behavior in social networks.…”

Section: Continuousmentioning

confidence: 99%

A Dynamic Emotional Propagation Model over Time for Competitive Environments

Chen,

Xu,

Cai

et al. 2023

Electronics

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Emotional propagation research aims to discover and show the laws of opinion evolution in social networks. The short-term observation of the emotional propagation process for a predetermined time window ignores situations in which users with different emotions compete over a long diffusion time. To that end, we propose a dynamic emotional propagation model based on an independent cascade. The proposed model is inspired by the interpretable factors of the reinforced Poisson process, portraying the “rich-get-richer” phenomenon within a social network. Specifically, we introduce a time-decay mechanism to illustrate the change in influence over time. Meanwhile, we propose an emotion-exciting mechanism allowing prior users to affect the emotions of subsequent users. Finally, we conduct experiments on an artificial network and two real-world datasets—Wiki, with 7194 nodes, and Bitcoin-OTC, with 5881 nodes—to verify the effectiveness of our proposed model. The proposed method improved the F1-score by 3.5% and decreased the MAPE by 0.059 on the Wiki dataset. And the F1-score improved by 0.4% and the MAPE decreased by 0.013 on the Bitcoin-OTC dataset. In addition, the experimental results indicate a phenomenon of emotions in social networks tending to converge under the influence of opinion leaders after a long enough time.

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“…Online social networks (OSN) play a crucial role in our daily lives [1][2][3]. Tey help people gather, communicate, and share their common interests [4].…”

Section: Introductionmentioning

confidence: 99%

CNFRD: A Few‐Shot Rumor Detection Framework via Capsule Network for COVID‐19

Chen

et al. 2023

International Journal of Intelligent Systems

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In recent years, COVID-19 has become the hottest topic. Various issues, such as epidemic transmission routes and preventive measures, have “occupied” several online social media platforms. Many rumors about COVID-19 have also arisen, causing public anxiety and seriously affecting normal social order. Identifying a rumor at its very inception is crucial to reducing the potential harm of its evolution to society as a whole. However, epidemic rumors provide limited signal features in the early stage. In order to identify rumors with data sparsity, we propose a few-shot learning rumor detection model based on capsule networks (CNFRD), utilizing the metric learning framework and the capsule network to detect the rumors posted during unexpected epidemic events. Specifically, we constructively use the capsule network neural layer to summarize the historical rumor data and obtain the generalized class representation based on the historical rumor data samples. Besides, we calculate the distance between the epidemic rumor sample and the historical rumor class-wise representation according to the metric module. Finally, epidemic rumors are discriminated against according to the nearest neighbor principle. The experimental results prove that the proposed method can achieve higher accuracy with fewer epidemic rumor samples. This approach provided 88.92% accuracy on the Chinese rumor dataset and 87.07% accuracy on the English rumor dataset, which improved by 7% to 23% over existing approaches. Therefore, the CNFRD model can identify epidemic rumors in COVID-19 as early as possible and effectively improve the performance of rumor detection.

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An emotion role mining approach based on multiview ensemble learning in social networks

Cited by 22 publications

References 37 publications

Graph Regularization Methods in Soft Detector Fusion

Graph Regularization Methods in Soft Detector Fusion

A Dynamic Emotional Propagation Model over Time for Competitive Environments

CNFRD: A Few‐Shot Rumor Detection Framework via Capsule Network for COVID‐19

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