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Psycho-Informatics: Big Data shaping modern psychometrics

Overview of attention for article published in Medical Hypotheses, January 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#15 of 2,087)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
10 news outlets
blogs
2 blogs
twitter
8 tweeters
weibo
1 weibo user
googleplus
1 Google+ user

Readers on

mendeley
186 Mendeley
citeulike
2 CiteULike
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Title
Psycho-Informatics: Big Data shaping modern psychometrics
Published in
Medical Hypotheses, January 2014
DOI 10.1016/j.mehy.2013.11.030
Pubmed ID
Authors

Alexander Markowetz, Konrad Błaszkiewicz, Christian Montag, Christina Switala, Thomas Schläpfer, Markowetz A, Błaszkiewicz K, Montag C, Switala C, Schlaepfer TE, Thomas E. Schlaepfer

Abstract

For the first time in history, it is possible to study human behavior on great scale and in fine detail simultaneously. Online services and ubiquitous computational devices, such as smartphones and modern cars, record our everyday activity. The resulting Big Data offers unprecedented opportunities for tracking and analyzing behavior. This paper hypothesizes the applicability and impact of Big Data technologies in the context of psychometrics both for research and clinical applications. It first outlines the state of the art, including the severe shortcomings with respect to quality and quantity of the resulting data. It then presents a technological vision, comprised of (i) numerous data sources such as mobile devices and sensors, (ii) a central data store, and (iii) an analytical platform, employing techniques from data mining and machine learning. To further illustrate the dramatic benefits of the proposed methodologies, the paper then outlines two current projects, logging and analyzing smartphone usage. One such study attempts to thereby quantify severity of major depression dynamically; the other investigates (mobile) Internet Addiction. Finally, the paper addresses some of the ethical issues inherent to Big Data technologies. In summary, the proposed approach is about to induce the single biggest methodological shift since the beginning of psychology or psychiatry. The resulting range of applications will dramatically shape the daily routines of researches and medical practitioners alike. Indeed, transferring techniques from computer science to psychiatry and psychology is about to establish Psycho-Informatics, an entire research direction of its own.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 186 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 3%
Germany 5 3%
United Kingdom 5 3%
India 2 1%
Switzerland 2 1%
France 2 1%
Malaysia 1 <1%
Brazil 1 <1%
China 1 <1%
Other 1 <1%
Unknown 160 86%

Demographic breakdown

Readers by professional status Count As %
Student > Master 38 20%
Student > Ph. D. Student 37 20%
Student > Bachelor 26 14%
Researcher 24 13%
Student > Doctoral Student 13 7%
Other 48 26%
Readers by discipline Count As %
Psychology 49 26%
Computer Science 45 24%
Medicine and Dentistry 24 13%
Unspecified 14 8%
Social Sciences 11 6%
Other 43 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 103. 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 27 February 2017.
All research outputs
#71,366
of 7,822,782 outputs
Outputs from Medical Hypotheses
#15
of 2,087 outputs
Outputs of similar age
#2,043
of 186,512 outputs
Outputs of similar age from Medical Hypotheses
#1
of 49 outputs
Altmetric has tracked 7,822,782 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,087 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.6. This one has done particularly well, scoring higher than 99% 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 186,512 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 98% of its contemporaries.
We're also able to compare this research output to 49 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 97% of its contemporaries.