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Consensus dynamics in online collaboration systems

Overview of attention for article published in Computational Social Networks, February 2018
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)

Mentioned by

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6 X users

Citations

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6 Dimensions

Readers on

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15 Mendeley
Title
Consensus dynamics in online collaboration systems
Published in
Computational Social Networks, February 2018
DOI 10.1186/s40649-018-0050-1
Pubmed ID
Authors

Ilire Hasani-Mavriqi, Dominik Kowald, Denis Helic, Elisabeth Lex

Abstract

In this paper, we study the process of opinion dynamics and consensus building in online collaboration systems, in which users interact with each other following their common interests and their social profiles. Specifically, we are interested in how users similarity and their social status in the community, as well as the interplay of those two factors, influence the process of consensus dynamics. For our study, we simulate the diffusion of opinions in collaboration systems using the well-known Naming Game model, which we extend by incorporating an interaction mechanism based on user similarity and user social status. We conduct our experiments on collaborative datasets extracted from the Web. Our findings reveal that when users are guided by their similarity to other users, the process of consensus building in online collaboration systems is delayed. A suitable increase of influence of user social status on their actions can in turn facilitate this process. In summary, our results suggest that achieving an optimal consensus building process in collaboration systems requires an appropriate balance between those two factors.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 2 13%
Student > Master 2 13%
Student > Postgraduate 2 13%
Researcher 2 13%
Professor 1 7%
Other 1 7%
Unknown 5 33%
Readers by discipline Count As %
Computer Science 3 20%
Business, Management and Accounting 1 7%
Nursing and Health Professions 1 7%
Agricultural and Biological Sciences 1 7%
Social Sciences 1 7%
Other 2 13%
Unknown 6 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 September 2020.
All research outputs
#6,200,735
of 23,018,998 outputs
Outputs from Computational Social Networks
#12
of 40 outputs
Outputs of similar age
#127,183
of 440,103 outputs
Outputs of similar age from Computational Social Networks
#2
of 2 outputs
Altmetric has tracked 23,018,998 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 40 research outputs from this source. They receive a mean Attention Score of 3.9. This one scored the same or higher as 28 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 440,103 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.