ArticlePloS one2015
Exploring the complex pattern of information spreading in online blog communities.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Metaphor Diffusion in Online Health Communities: Infodemiology Study in a Stroke Online Health Community.JMIR cardio · 2024Article
- Influencer identification in dynamical complex systems.Journal of complex networks · 2020Review
- Identifying critical edges in complex networks.Scientific reports · 2018Article
- The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions.PloS one · 2018Article
- Shortest Paths in Multiplex Networks.Scientific reports · 2017Article
- Efficient collective influence maximization in cascading processes with first-order transitions.Scientific reports · 2017Article
- Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks.Scientific reports · 2016Article
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Authors and funding
5 authors.
Funding
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Abstract
Information spreading in online social communities has attracted tremendous attention due to its utmost practical values in applications. Despite that several individual-level diffusion data have been investigated, we still lack the detailed understanding of the spreading pattern of information. Here, by comparing information flows and social links in a blog community, we find that the diffusion processes are induced by three different spreading mechanisms: social spreading, self-promotion and broadcast. Although numerous previous studies have employed epidemic spreading models to simulate information diffusion, we observe that such models fail to reproduce the realistic diffusion pattern. In respect to users behaviors, strikingly, we find that most users would stick to one specific diffusion mechanism. Moreover, our observations indicate that the social spreading is not only crucial for the structure of diffusion trees, but also capable of inducing more subsequent individuals to acquire the information. Our findings suggest new directions for modeling of information diffusion in social systems, and could inform design of efficient propagation strategies based on users behaviors.
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Registered trials
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