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Scaling-Laws of Human Broadcast Communication Enable Distinction between Human, Corporate and Robot Twitter Users

Overview of attention for article published in PLOS ONE, July 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
6 news outlets
blogs
2 blogs
twitter
136 X users
facebook
8 Facebook pages
googleplus
6 Google+ users

Citations

dimensions_citation
46 Dimensions

Readers on

mendeley
87 Mendeley
citeulike
2 CiteULike
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Title
Scaling-Laws of Human Broadcast Communication Enable Distinction between Human, Corporate and Robot Twitter Users
Published in
PLOS ONE, July 2013
DOI 10.1371/journal.pone.0065774
Pubmed ID
Authors

Gabriela Tavares, Aldo Faisal

Abstract

Human behaviour is highly individual by nature, yet statistical structures are emerging which seem to govern the actions of human beings collectively. Here we search for universal statistical laws dictating the timing of human actions in communication decisions. We focus on the distribution of the time interval between messages in human broadcast communication, as documented in Twitter, and study a collection of over 160,000 tweets for three user categories: personal (controlled by one person), managed (typically PR agency controlled) and bot-controlled (automated system). To test our hypothesis, we investigate whether it is possible to differentiate between user types based on tweet timing behaviour, independently of the content in messages. For this purpose, we developed a system to process a large amount of tweets for reality mining and implemented two simple probabilistic inference algorithms: 1. a naive Bayes classifier, which distinguishes between two and three account categories with classification performance of 84.6% and 75.8%, respectively and 2. a prediction algorithm to estimate the time of a user's next tweet with an R(2) ≈ 0.7. Our results show that we can reliably distinguish between the three user categories as well as predict the distribution of a user's inter-message time with reasonable accuracy. More importantly, we identify a characteristic power-law decrease in the tail of inter-message time distribution by human users which is different from that obtained for managed and automated accounts. This result is evidence of a universal law that permeates the timing of human decisions in broadcast communication and extends the findings of several previous studies of peer-to-peer communication.

X Demographics

X Demographics

The data shown below were collected from the profiles of 136 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 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 2 2%
United Kingdom 2 2%
Germany 1 1%
Malaysia 1 1%
France 1 1%
Japan 1 1%
United States 1 1%
Luxembourg 1 1%
Unknown 77 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 24%
Student > Master 21 24%
Researcher 14 16%
Professor > Associate Professor 6 7%
Other 4 5%
Other 9 10%
Unknown 12 14%
Readers by discipline Count As %
Computer Science 26 30%
Social Sciences 13 15%
Business, Management and Accounting 6 7%
Mathematics 5 6%
Physics and Astronomy 5 6%
Other 19 22%
Unknown 13 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 178. 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 13 August 2016.
All research outputs
#233,374
of 25,901,238 outputs
Outputs from PLOS ONE
#3,394
of 225,915 outputs
Outputs of similar age
#1,481
of 207,402 outputs
Outputs of similar age from PLOS ONE
#83
of 4,809 outputs
Altmetric has tracked 25,901,238 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 225,915 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.8. This one has done particularly well, scoring higher than 98% 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 207,402 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 99% of its contemporaries.
We're also able to compare this research output to 4,809 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 98% of its contemporaries.