An overview of structurally complex network-based modeling of public opinion in the “We the Media” era

Wang, Guanghui; Wang, Yufei; Liu, Yijun; Chi, Yuxue

doi:10.1142/s0217979218300062

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…It may also help in characterizing the application leading to the provision of deeper insight into the structure and patterns of data [17]. Network based modeling has been used for a variety of research areas from social sciences to engineering and technology, including public opinion transmission [50], financial systems [52], biomedical process and systems [19], [77], design and manufacturing [125], Genetics [83], Microbiology [119], Strategic relationships [86], geomagnetic fluctuations [131], traffic networks [58] wireless network, sensor network, smart grids, supply chain, transportation systems [99], negotiation methods for suppliers [105] and big data mining [74].…”

Section: Network-based Modelingmentioning

confidence: 99%

Hierarchical Modeling of Complex Internet of Things Systems Using Conceptual Modeling Approaches

2019

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Popularity of the Internet of things (IoT) is currently on the rise. Academia as well as industry is equally fascinated by this disruptive technology. The IoT research and implementation have succeeded securing their place in numerous domains including, but not limited to, business, transportation and logistics, health care, smart cities, and agriculture. Conceptual modeling is pivotal to the design and implementation of a system. Various modeling approaches have been used for a variety of the IoT applications and case studies. In this paper, we examine various innovative applications of the existing conceptual modeling approaches for the IoT. We analyze the use of these approaches against different concerns in isolation as well as in mutual combination for the IoT. We also define a set of terminologies that we frequently encounter in this manuscript and those likely to be used while adopting proposed modeling approaches for the IoT scenarios. This study also attempts to reconcile different modeling approaches toward a unified modeling system. For this purpose, we map various conceptual modeling approaches against different layers of functional model of the IoT reference architecture. There is also a scenario in the end of the paper which elaborates the use of different modeling approaches in hierarchy for a complex IoT system. INDEX TERMS Agent-based modeling, ambient-oriented modeling, aspect-oriented modeling, conceptual modeling approaches, contract-based modeling, fuzzy-logic modeling, the Internet of Things, network-based modeling, object-based modeling, ontology, service-oriented modeling.

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Section: Network-based Modelingmentioning

confidence: 99%

Hierarchical Modeling of Complex Internet of Things Systems Using Conceptual Modeling Approaches

2019

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“…Guanghui, W. et al based on the perspective of the development and evolution of complex networks. The perspective of the development and evolution of complex networks studied the modeling of Internet public opinion based on structurally complex networks in the era of self-media and provided a comprehensive summary of the progress of research projects related to the structural modeling of Internet public opinion, aiming to lay the foundation for the study of the evolution of Internet public opinion and its dissemination in the era of self-media [17]. Schwarzenegger, C. explores the impact of "fake news and post-truth narratives" on media users' personal epistemology, media beliefs, and news navigation practices based on a media library perspective.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Self-Media on Mainstream Media News Dissemination in the Perspective of New Media

Wang

2024

Applied Mathematics and Nonlinear Sciences

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In this paper, web crawler technology is used to obtain research data on news dissemination of self media and mainstream media, data cleaning is performed on the initial data through Pandas package, and the processed research data is stored in the database. From the acquired research data of self media and mainstream media news dissemination, the independent variables and dependent variables are determined to complete the construction of linear regression model of mainstream media news dissemination, and the influence of self media on mainstream media news dissemination is judged by calculating the correlation coefficients of the linear regression model. The results show that compared with X1, X3 (Bate=0.223, p<0.01) and X7 (Bate=−0.016, p<0.01), have a significant positive effect on the overall communication effect of mainstream media, indicating that the comprehensive population coverage and communication speed of self media undoubtedly bring considerable influence on the news dissemination of mainstream media. This study hopes to provide theoretical support for the research of related workers. It significantly promotes the joint development of self media and mainstream media news dissemination.

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“…The centrality measure effectively quantifies the network's influential nodes. Identifying powerful or vital nodes is critical in many networking applications, including disease research [1], [2], fake news spreading [3], viral marketing [4], drug development, opinion monitoring [5], biological field [6], fraud detection [7], structural design [8],…”

Section: Introductionmentioning

confidence: 99%

Generalization of Relative Change in a Centrality Measure to Identify Vital Nodes in Complex Networks

et al. 2023

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Identifying vital nodes is important in disease research, spreading rumors, viral marketing, and drug development. The vital nodes in any network are used to spread information as widely as possible. Centrality measures such as Degree centrality (D), Betweenness centrality (B), Closeness centrality (C), Katz (K), Cluster coefficient (CC), PR (PageRank), LGC (Local and Global Centrality), ISC (Isolating Centrality) centrality measures can be used to effectively quantify vital nodes. The majority of these centrality measures are defined in the literature and are based on a network's local and/or global structure. However, these measures are time-consuming and inefficient for large-scale networks. Also, these measures cannot study the effect of removal of vital nodes in resource-constrained networks. To address these concerns, we propose the six new centrality measures namely GRACC, LRACC, GRAD, LRAD, GRAK, and LRAK. We develop these measures based on the relative change of the clustering coefficient, degree, and Katz centralities after the removal of a vertex. Next, we compare the proposed centrality measures with D, B, C, CC, K, PR, LGC, and ISC to demonstrate their efficiency and time complexity. We utilize the SIR (Susceptible-Infected-Recovered) and IC (Independent Cascade) models to study the maximum information spread of proposed measures over conventional ones. We perform extensive simulations on large-scale real-world data sets and prove that local centrality measures perform better in some networks than global measures in terms of time complexity and information spread. Further, we also observe the number of cliques drastically improves the efficiency of global centrality measures.INDEX TERMS Complex networks, influential nodes, local centrality, relative change in centrality.

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An overview of structurally complex network-based modeling of public opinion in the “We the Media” era

Cited by 8 publications

References 39 publications

Hierarchical Modeling of Complex Internet of Things Systems Using Conceptual Modeling Approaches

Hierarchical Modeling of Complex Internet of Things Systems Using Conceptual Modeling Approaches

The Influence of Self-Media on Mainstream Media News Dissemination in the Perspective of New Media

Generalization of Relative Change in a Centrality Measure to Identify Vital Nodes in Complex Networks

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