↓ Skip to main content

Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks

Overview of attention for article published in PLoS ONE, April 2014
Altmetric Badge

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 (99th percentile)

Mentioned by

news
9 news outlets
blogs
4 blogs
twitter
189 tweeters
facebook
2 Facebook pages
wikipedia
1 Wikipedia page
googleplus
9 Google+ users

Citations

dimensions_citation
43 Dimensions

Readers on

mendeley
152 Mendeley
citeulike
3 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks
Published in
PLoS ONE, April 2014
DOI 10.1371/journal.pone.0092413
Pubmed ID
Authors

Manuel Garcia-Herranz, Esteban Moro, Manuel Cebrian, Nicholas A. Christakis, James H. Fowler

Abstract

Recent research has focused on the monitoring of global-scale online data for improved detection of epidemics, mood patterns, movements in the stock market political revolutions, box-office revenues, consumer behaviour and many other important phenomena. However, privacy considerations and the sheer scale of data available online are quickly making global monitoring infeasible, and existing methods do not take full advantage of local network structure to identify key nodes for monitoring. Here, we develop a model of the contagious spread of information in a global-scale, publicly-articulated social network and show that a simple method can yield not just early detection, but advance warning of contagious outbreaks. In this method, we randomly choose a small fraction of nodes in the network and then we randomly choose a friend of each node to include in a group for local monitoring. Using six months of data from most of the full Twittersphere, we show that this friend group is more central in the network and it helps us to detect viral outbreaks of the use of novel hashtags about 7 days earlier than we could with an equal-sized randomly chosen group. Moreover, the method actually works better than expected due to network structure alone because highly central actors are both more active and exhibit increased diversity in the information they transmit to others. These results suggest that local monitoring is not just more efficient, but also more effective, and it may be applied to monitor contagious processes in global-scale networks.

Twitter Demographics

The data shown below were collected from the profiles of 189 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 152 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 152 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 3 2%
Student > Ph. D. Student 2 1%
Student > Bachelor 2 1%
Professor > Associate Professor 2 1%
Researcher 1 <1%
Other 1 <1%
Unknown 141 93%
Readers by discipline Count As %
Unspecified 5 3%
Agricultural and Biological Sciences 3 2%
Medicine and Dentistry 2 1%
Social Sciences 1 <1%
Unknown 141 93%

Attention Score in Context

This research output has an Altmetric Attention Score of 251. 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 16 May 2016.
All research outputs
#43,002
of 12,470,444 outputs
Outputs from PLoS ONE
#958
of 136,800 outputs
Outputs of similar age
#692
of 191,900 outputs
Outputs of similar age from PLoS ONE
#34
of 4,516 outputs
Altmetric has tracked 12,470,444 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 136,800 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.7. 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 191,900 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 4,516 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 99% of its contemporaries.