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Modeling topic control to detect influence in conversations using nonparametric topic models

Overview of attention for article published in Machine Learning, October 2013
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Mentioned by

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1 X user

Citations

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15 Dimensions

Readers on

mendeley
85 Mendeley
Title
Modeling topic control to detect influence in conversations using nonparametric topic models
Published in
Machine Learning, October 2013
DOI 10.1007/s10994-013-5417-9
Authors

Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, Deborah A. Cai, Jennifer E. Midberry, Yuanxin Wang

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
France 1 1%
Unknown 83 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 36%
Student > Master 17 20%
Researcher 6 7%
Student > Bachelor 6 7%
Lecturer 5 6%
Other 12 14%
Unknown 8 9%
Readers by discipline Count As %
Computer Science 44 52%
Social Sciences 11 13%
Business, Management and Accounting 6 7%
Linguistics 5 6%
Engineering 3 4%
Other 6 7%
Unknown 10 12%
Attention Score in Context

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 30 October 2013.
All research outputs
#18,353,475
of 22,729,647 outputs
Outputs from Machine Learning
#870
of 952 outputs
Outputs of similar age
#157,964
of 212,101 outputs
Outputs of similar age from Machine Learning
#8
of 8 outputs
Altmetric has tracked 22,729,647 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 952 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 2nd percentile – i.e., 2% of its peers scored the same or lower than it.
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 212,101 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.