• Corpus ID: 16418133

Model of Opinion Spreading in Social Networks

@article{Kanovsky2011ModelOO,
  title={Model of Opinion Spreading in Social Networks},
  author={Igor Kanovsky and Omer Yaary},
  journal={ArXiv},
  year={2011},
  volume={abs/1106.0872}
}
We proposed a new model, which capture the main difference between information and opinion spreading. In information spreading additional exposure to certain information has a small effect. Contrary, when an actor is exposed to 2 opinioned actors the probability to adopt the opinion is significant higher than in the case of contact with one such actor (called by J. Kleinberg "the 0-1-2 effect"). In each time step if an actor does not have an opinion, we randomly choose 2 his network neighbors… 
Research on the Community Number Evolution Model of Public Opinion Based on Stochastic Competitive Learning
TLDR
This study proposed the community number evolution model of public opinion based on stochastic competitive learning, and the proposed model consists of an increase in the number of communities and a decrease in theNumber of communities.
TweetGames: A framework for twitter-based collaborative social online games
TLDR
The intention of the framework is to provide a useful and powerful tool to complex social network researchers to study emergent and collective user behavior on a large scale utilizing the huge user base of a social network service like Twitter.

References

SHOWING 1-8 OF 8 REFERENCES
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters
TLDR
This paper employs approximation algorithms for the graph-partitioning problem to characterize as a function of size the statistical and structural properties of partitions of graphs that could plausibly be interpreted as communities, and defines the network community profile plot, which characterizes the "best" possible community—according to the conductance measure—over a wide range of size scales.
Graphs over time: densification laws, shrinking diameters and possible explanations
TLDR
A new graph generator is provided, based on a "forest fire" spreading process, that has a simple, intuitive justification, requires very few parameters (like the "flammability" of nodes), and produces graphs exhibiting the full range of properties observed both in prior work and in the present study.
Complex Contagions and the Weakness of Long Ties1
The strength of weak ties is that they tend to be long—they connect socially distant locations, allowing information to diffuse rapidly. The authors test whether this “strength of weak ties”
Identification of influential spreaders in complex networks
Spreading of information, ideas or diseases can be conveniently modelled in the context of complex networks. An analysis now reveals that the most efficient spreaders are not always necessarily the
Personal Influence: The Part Played by People in the Flow of Mass Communications
First published in 1955, "Personal Influence" reports the results of a pioneering study conducted in Decatur, Illinois, validating Paul Lazarsfeld's serendipitous discovery that messages from the
The structure of scientific collaboration networks.
  • M. Newman
  • Physics
    Proceedings of the National Academy of Sciences of the United States of America
  • 2001
TLDR
It is shown that these collaboration networks form "small worlds," in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances.
The convergence of social and technological networks
TLDR
Internet-based data on human interaction connects scientific inquiry like never before and helps scientists understand the world around us more fully.
The accidental influentials