| Title |
Why continuous discussion can promote the consensus of opinions?
|
|---|---|
| Published in |
Computational Social Networks, November 2016
|
| DOI | 10.1186/s40649-016-0035-x |
| Pubmed ID | |
| Authors |
Zhenpeng Li, Xijin Tang, Benhui Chen, Jian Yang, Peng Su |
| Abstract |
Why group opinions tend to be converged through continued communication, discussion and interactions? Under the framework of the social influence network model, we rigorously prove that the group consensus is almost surely within finite steps. This is a quite certain result, and reflects the real-world common phenomenon. In addition, we give a convergence time lower bound. Although our explanations are purely based on mathematic deduction, it shows that the latent social influence structure is the key factor for the persistence of disagreement and formation of opinions convergence or consensus in the real world social system. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 8 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 38% |
| Researcher | 1 | 13% |
| Lecturer | 1 | 13% |
| Student > Postgraduate | 1 | 13% |
| Unknown | 2 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 1 | 13% |
| Agricultural and Biological Sciences | 1 | 13% |
| Computer Science | 1 | 13% |
| Decision Sciences | 1 | 13% |
| Social Sciences | 1 | 13% |
| Other | 0 | 0% |
| Unknown | 3 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 December 2016.
All research outputs
#18,498,050
of 22,919,505 outputs
Outputs from Computational Social Networks
#35
of 40 outputs
Outputs of similar age
#302,790
of 414,948 outputs
Outputs of similar age from Computational Social Networks
#3
of 3 outputs
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