Analysis of competitive information diffusion in a group-based population over social networks

Fu, Guiyuan; Chen, Feier; Liu, Jianguo; Han, Jingti

doi:10.1016/j.physa.2019.03.035

Cited by 26 publications

(31 citation statements)

References 29 publications

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“…tags, user-expressed subjective opinions, ratings, user profiles and both explicit and implicit social networks) whilst the latter is mainly based on the study of user-to-user relationships. In this field, we can mention the approaches for brand advocacy (Schepers and Nijssen 2018 ; Liu et al 2017a ), reputation management (Budak et al 2011b ; Liu et al 2019 ), competitor analysis (Valera et al 2014 ; Fu et al 2019 ), community management (Dholakia and Vianello 2009 ), customer management (Mossel and Tamuz 2012 ), viral marketing (Lu et al 2013b ; Maurer and Wiegmann 2011 ; Trattner and Kappe 2013 ) and sentiment analysis (Mäntylä et al 2018 ; Jiménez et al 2019 ) to cite a few. The expected outputs of these analyses are information about: Why people like or dislike a product?…”

Section: Introductionmentioning

confidence: 99%

A survey of Big Data dimensions vs Social Networks analysis

2020

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The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated data (text, video, image and audio), data heterogeneity and high speed generation rate. More in detail, the analysis of user generated data by popular social networks (i.e Facebook (https://www.facebook.com/), Twitter (https://www.twitter.com/), Instagram (https://www.instagram.com/), LinkedIn (https://www.linkedin.com/)) poses quite intriguing challenges for both research and industry communities in the task of analyzing user behavior, user interactions, link evolution, opinion spreading and several other important aspects. This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V’s).

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Section: Introductionmentioning

confidence: 99%

A survey of Big Data dimensions vs Social Networks analysis

2020

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“…Emotion conveyed by each piece of information: this includes disputes over a specific theme ( Fu et al., 2019 ) or thought, including positive and negative information ( Zhu et al., 2020 ).…”

Section: Multipoint Model Of Product Information Diffusionmentioning

confidence: 99%

Product information diffusion model and reasoning process in consumer behavior

Sun

Hou

Cai

et al. 2020

Heliyon

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“…Thus, some studies have proposed models of information diffusion which focused on the diffusion of competing information commonly appearing in social networks. For example, Fu et al divided the population into three subgroups and proposed a modified SIR model for competitive information diffusion among group-based population over social networks [21]. Yan et al introduced a diffusion model to explain the competitive diffusion of repurchased products in knowledgeable manufacturing but not in social networks [22].…”

Section: Et Al Considered Thementioning

confidence: 99%

A Model for Competing Information Diffusion in Social Networks

Sun

Lü

et al. 2019

IEEE Access

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In recent years, social networks have attracted the interest of researchers from diverse disciplines. Competing types of information, such as positive and negative information about a topic or marketing information for similar products from different companies, often diffuse in social networks simultaneously. However, most previous studies only consider one type of information using the susceptible-infectiousrecovered or susceptible-infectious-susceptible models. In this paper, we propose a competitive diffusion model to describe the diffusion processes of two types of information in social networks, analyze the stability of the diffusion infection-free equilibrium, and study the diffusion processes by numerical simulations. The results show that the information with the larger probabilities of being received and reposted diffuses more broadly and suppresses the competitor. We also find that the initial number of spreader nodes, the degrees of participants, and network structures all affect the scope of information diffusion.

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Analysis of competitive information diffusion in a group-based population over social networks

Cited by 26 publications

References 29 publications

A survey of Big Data dimensions vs Social Networks analysis

A survey of Big Data dimensions vs Social Networks analysis

Product information diffusion model and reasoning process in consumer behavior

A Model for Competing Information Diffusion in Social Networks

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