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    谭振华, 时迎成, 石楠翔, 杨广明, 王兴伟. 基于引力学的在线社交网络空间谣言传播分析模型[J]. 计算机研究与发展, 2017, 54(11): 2586-2599. DOI: 10.7544/issn1000-1239.2017.20160434
    引用本文: 谭振华, 时迎成, 石楠翔, 杨广明, 王兴伟. 基于引力学的在线社交网络空间谣言传播分析模型[J]. 计算机研究与发展, 2017, 54(11): 2586-2599. DOI: 10.7544/issn1000-1239.2017.20160434
    Tan Zhenhua, Shi Yingcheng, Shi Nanxiang, Yang Guangming, Wang Xingwei. Rumor Propagation Analysis Model Inspired by Gravity Theory for Online Social Networks[J]. Journal of Computer Research and Development, 2017, 54(11): 2586-2599. DOI: 10.7544/issn1000-1239.2017.20160434
    Citation: Tan Zhenhua, Shi Yingcheng, Shi Nanxiang, Yang Guangming, Wang Xingwei. Rumor Propagation Analysis Model Inspired by Gravity Theory for Online Social Networks[J]. Journal of Computer Research and Development, 2017, 54(11): 2586-2599. DOI: 10.7544/issn1000-1239.2017.20160434

    基于引力学的在线社交网络空间谣言传播分析模型

    Rumor Propagation Analysis Model Inspired by Gravity Theory for Online Social Networks

    • 摘要: 社交网络空间的谣言传播行为具有极大的危害性,探索谣言传播规律与分析模型成为当前研究的热点之一.传统谣言传播分析模型大都基于SIR等传染病传播模型,能对在线社交网络空间的谣言传播过程进行粗粒度刻画,但并未充分考虑社交网络本身特征.鉴于此,结合引力学思想,提出了一种新的在线社交网络空间谣言传播分析模型GRPModel.该模型借鉴引力学思想,从用户和谣言信息2个角度出发,探索谣言在用户间的传播规律.以用户为核心,基于用户间的关系、信息在用户间的传播关系、谣言接触率、转发率等对用户影响力、谣言影响力进行建模,对谣言信息的传播进行量化,并充分考虑用户的个性化特征,构建相应的建模与分析函数.最后利用新浪微博真实社交网络空间信息,对GRPModel进行分析验证,验证结果证明了所做模型的正确性和有效性.

       

      Abstract: The influence of rumor propagation in online social networks (OSN) could result in great damage to social life, and it has been a hot topic to discover rumor propagation pattern. Traditional Epidemic-like rumor propagation models based on SIR, are generally coarse-grained for OSN but do not fully consider the features of OSN, such as personalization dimensions of users' behavior and information attributes. Inspired by gravity theory, this paper proposes a novel rumor propagation analysis model named gravity-inspired rumor propagation model (GRPModel), and tries to find a new pattern of rumor propagation from the perspectives both of users' properties and rumors' attributes. In GRPModel, user influence and rumor influence are modeled mathematically by user relations and information attributes, and fully consider their personalized features. We collect experimental real data from Sina Weibo, which is a famous OSN in China, and investigate features of users and real rumors. Experiments prove the effectiveness and efficiency.

       

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