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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Leveraging Position Bias to Improve Peer Recommendation
|
|---|---|
| Published in |
PLOS ONE, June 2014
|
| DOI | 10.1371/journal.pone.0098914 |
| Pubmed ID | |
| Authors |
Kristina Lerman, Tad Hogg |
| Abstract |
With the advent of social media and peer production, the amount of new online content has grown dramatically. To identify interesting items in the vast stream of new content, providers must rely on peer recommendation to aggregate opinions of their many users. Due to human cognitive biases, the presentation order strongly affects how people allocate attention to the available content. Moreover, we can manipulate attention through the presentation order of items to change the way peer recommendation works. We experimentally evaluate this effect using Amazon Mechanical Turk. We find that different policies for ordering content can steer user attention so as to improve the outcomes of peer recommendation. |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 67% |
| India | 1 | 11% |
| Unknown | 2 | 22% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 56% |
| Scientists | 4 | 44% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 2 | 3% |
| Australia | 2 | 3% |
| United Kingdom | 1 | 2% |
| Unknown | 57 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 24% |
| Researcher | 9 | 15% |
| Student > Bachelor | 7 | 11% |
| Student > Doctoral Student | 4 | 6% |
| Professor > Associate Professor | 4 | 6% |
| Other | 11 | 18% |
| Unknown | 12 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 21 | 34% |
| Business, Management and Accounting | 5 | 8% |
| Social Sciences | 3 | 5% |
| Agricultural and Biological Sciences | 2 | 3% |
| Economics, Econometrics and Finance | 2 | 3% |
| Other | 16 | 26% |
| Unknown | 13 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 66. 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 08 March 2018.
All research outputs
#639,600
of 25,193,883 outputs
Outputs from PLOS ONE
#8,651
of 218,525 outputs
Outputs of similar age
#5,815
of 234,871 outputs
Outputs of similar age from PLOS ONE
#187
of 4,375 outputs
Altmetric has tracked 25,193,883 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 218,525 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.7. This one has done particularly well, scoring higher than 96% of its peers.
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 234,871 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 4,375 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.