An information diffusion model based on retweeting mechanism for online social media

Xiong, Fei; Liu, Yun; Zhang, Zhenjiang; Zhu, Jiasong; Zhang, Ying

doi:10.1016/j.physleta.2012.05.021

Cited by 155 publications

(73 citation statements)

References 30 publications

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“…Zhao et al [23] proposed an SIHR (Spreaders, Ignorants, Hibernators, Removed) rumor spreading model, with forgetting and remembering mechanisms to simulate rumor spreading in inhomogeneous networks. Xiong et al [20] proposed a diffusion model with four different states: susceptible, contacted, infected, and refractory (SCIR) to identify the threshold value of the spreading rate approaches almost zero. Bettencourt et al [1] proposed the SEIZ (susceptible, exposed, infected, skeptic) model to capture the adoption of Feynman diagrams by using the publication counts after World War II.…”

Section: Epidemiological Modelsmentioning

confidence: 99%

Epidemiological modeling of news and rumors on Twitter

Jin

Dougherty

Saraf

et al. 2013

Proceedings of the 7th Workshop on Social Network Mining and Analysis

285

182

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Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.

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Section: Epidemiological Modelsmentioning

confidence: 99%

Epidemiological modeling of news and rumors on Twitter

Jin

Dougherty

Saraf

et al. 2013

Proceedings of the 7th Workshop on Social Network Mining and Analysis

285

182

View full text Add to dashboard Cite

show abstract

“…Zhu, Wang, Wu, and Zhu (2014), and Agarwal, Liu, Tang, and Yu (2008) studied the user influence in a social network, and analyze the relationship between user influence and their behavior, to find out the way to improve the influence of user and their microblogs. Li and Shiu (2012), Xiong, Liu, Zhang, Zhu, and Zhang (2012), Lei, Lin, and Wang (2013) and Wei, Bu, and Liang (2012) studied the information diffusion characteristic on microblogging site and analyze the impact of user behavior on diffusion effect.…”

Section: Literature Reviewmentioning

confidence: 99%

WITHDRAWN: The study on factors affecting the attraction of microblog

Song

2015

Computers in Human Behavior

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“…Social recommendation has become more and more important with the increasing popularity of social media and social networks [25,26]. Trust information indicates users' social relations, which are widely used in social recommendation models [27].…”

Section: Introductionmentioning

confidence: 99%

Diffusion-based recommendation with trust relations on tripartite graphs

et al. 2017

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Abstract. The diffusion-based recommendation approach is a vital branch in recommender systems, which successfully applies physical processes to make recommendations for users on bipartite or tripartite graphs. Trust links indicate users' social relations and can provide the benefit of reducing data sparsity. However, traditional diffusion-based algorithms only consider rating links when making recommendations. In this paper, the complementarity of users' implicit and explicit trust is exploited, and a novel resource-allocation strategy is proposed, which integrates these two kinds of trust relations on tripartite graphs. Through empirical studies on three benchmark datasets, our proposed method obtains better performance than most of the benchmark algorithms in terms of accuracy, diversity and novelty. According to the experimental results, our method is an efficient and reasonable way to integrate additional features into the diffusion-based recommendation approach.

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An information diffusion model based on retweeting mechanism for online social media

Cited by 155 publications

References 30 publications

Epidemiological modeling of news and rumors on Twitter

Epidemiological modeling of news and rumors on Twitter

WITHDRAWN: The study on factors affecting the attraction of microblog

Diffusion-based recommendation with trust relations on tripartite graphs

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