The Analysis of Influencing Factors of Information Dissemination on Cascade Size Distribution in Social Networks
The cognitive behavior of online user groups on information promotes the dissemination of information. The trajectories of information dissemination in social networks can be described by treelike cascades, and the distribution of the sizes of these cascades can capture the distribution of popularit...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8472147/ |
| _version_ | 1819171997536485376 |
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| author | Jian Dong Bin Chen Liang Liu Chuan Ai Fang Zhang |
| author_facet | Jian Dong Bin Chen Liang Liu Chuan Ai Fang Zhang |
| author_sort | Jian Dong |
| collection | DOAJ |
| description | The cognitive behavior of online user groups on information promotes the dissemination of information. The trajectories of information dissemination in social networks can be described by treelike cascades, and the distribution of the sizes of these cascades can capture the distribution of popularity of a social network. Numerous studies have shown that the cascade size distribution follows fat-tail distributions, including power-law distribution and bimodal distribution; however, the underlying characteristic of this highly skewed distribution lacks quantitative experimental analysis. Based on the stochastic epidemic-like information dissemination model, namely, the susceptible view forward removed model, this paper explores the impact of the content attractiveness and influence, and information source on the cascade size distribution through computational experiments. On the one hand, we find that when the mean value of the information influence and attractiveness is small, the cascade sizes follow a power-law distribution, and the larger the variance, the heavier the tail. On the other hand, the more random the distribution of information sources in social networks, the smaller the slope of the power-law cascade size distribution. Our findings quantitatively reveal the causality of power-law cascade size distribution from computational experiments, clarify the role of information attractiveness, influence, and sources on the distribution of popularity in social networks. |
| first_indexed | 2024-12-22T20:00:11Z |
| format | Article |
| id | doaj.art-9338e6df22374e9a94d8b6ae256cd476 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:00:11Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9338e6df22374e9a94d8b6ae256cd4762022-12-21T18:14:18ZengIEEEIEEE Access2169-35362018-01-016541855419410.1109/ACCESS.2018.28711458472147The Analysis of Influencing Factors of Information Dissemination on Cascade Size Distribution in Social NetworksJian Dong0https://orcid.org/0000-0002-3618-9082Bin Chen1https://orcid.org/0000-0002-2962-9254Liang Liu2Chuan Ai3Fang Zhang4College of System Engineering, National University of Defense Technology, Changsha, ChinaCollege of System Engineering, National University of Defense Technology, Changsha, ChinaCollege of System Engineering, National University of Defense Technology, Changsha, ChinaCollege of System Engineering, National University of Defense Technology, Changsha, ChinaCollege of System Engineering, National University of Defense Technology, Changsha, ChinaThe cognitive behavior of online user groups on information promotes the dissemination of information. The trajectories of information dissemination in social networks can be described by treelike cascades, and the distribution of the sizes of these cascades can capture the distribution of popularity of a social network. Numerous studies have shown that the cascade size distribution follows fat-tail distributions, including power-law distribution and bimodal distribution; however, the underlying characteristic of this highly skewed distribution lacks quantitative experimental analysis. Based on the stochastic epidemic-like information dissemination model, namely, the susceptible view forward removed model, this paper explores the impact of the content attractiveness and influence, and information source on the cascade size distribution through computational experiments. On the one hand, we find that when the mean value of the information influence and attractiveness is small, the cascade sizes follow a power-law distribution, and the larger the variance, the heavier the tail. On the other hand, the more random the distribution of information sources in social networks, the smaller the slope of the power-law cascade size distribution. Our findings quantitatively reveal the causality of power-law cascade size distribution from computational experiments, clarify the role of information attractiveness, influence, and sources on the distribution of popularity in social networks.https://ieeexplore.ieee.org/document/8472147/Attractivenesscascade size distributioninfluenceinformation sourcepower-law distribution |
| spellingShingle | Jian Dong Bin Chen Liang Liu Chuan Ai Fang Zhang The Analysis of Influencing Factors of Information Dissemination on Cascade Size Distribution in Social Networks IEEE Access Attractiveness cascade size distribution influence information source power-law distribution |
| title | The Analysis of Influencing Factors of Information Dissemination on Cascade Size Distribution in Social Networks |
| title_full | The Analysis of Influencing Factors of Information Dissemination on Cascade Size Distribution in Social Networks |
| title_fullStr | The Analysis of Influencing Factors of Information Dissemination on Cascade Size Distribution in Social Networks |
| title_full_unstemmed | The Analysis of Influencing Factors of Information Dissemination on Cascade Size Distribution in Social Networks |
| title_short | The Analysis of Influencing Factors of Information Dissemination on Cascade Size Distribution in Social Networks |
| title_sort | analysis of influencing factors of information dissemination on cascade size distribution in social networks |
| topic | Attractiveness cascade size distribution influence information source power-law distribution |
| url | https://ieeexplore.ieee.org/document/8472147/ |
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