CoChat
Sign Up
Cited by 304Complex Network Analysis TechniquesComplex Systems and Time Series AnalysisDigital Media and Philosophy

The DOI matches its Crossref record; 37 of the 38 references it cites were found in a registry or an index.

Accelerating dynamics of collective attention

Open access
Lorenz-Spreen, Philipp, Mønsted, Bjarke Mørch, Hövel, Philipp, Lehmann, Sune·2019·Nature Communications
DOI 10.1038/s41467-019-09311-wPMC PMC6465266
Open this paper in CoChat — free accountOpen PDF

Abstract

With news pushed to smart phones in real time and social media reactions spreading across the globe in seconds, the public discussion can appear accelerated and temporally fragmented. In longitudinal datasets across various domains, covering multiple decades, we find increasing gradients and shortened periods in the trajectories of how cultural items receive collective attention. Is this the inevitable conclusion of the way information is disseminated and consumed? Our findings support this hypothesis. Using a simple mathematical model of topics competing for finite collective attention, we are able to explain the empirical data remarkably well. Our modeling suggests that the accelerating ups and downs of popular content are driven by increasing production and consumption of content, resulting in a more rapid exhaustion of limited attention resources. In the interplay with competition for novelty, this causes growing turnover rates and individual topics receiving shorter intervals of collective attention.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-019-09311-w

Cites

37 of 37 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Measuring the speed of hashtag dynamics: a Daily usage Li(t) of the top 50 hashtags i on Twitter in 2016, with exemplary highlighted major events. b Average trajectories 〈L(t − tpeak)〉 leading to a local maximum Li(tpeak) in all top hashtags from 2013 to 2016. The shaded area shows the 95% confidence intervals (CI). In the background a 1% random sample of trajectories is shown in grey. c Temporal density of extreme events (local maxima with a lower threshold for the slopes, for details see Methods section), scattered over 365 days. d Distribution of daily relative losses P(ΔLi^(l)∕Li) and e daily relative gains P(ΔLi^(g)∕Li). The solid lines show fitted log-normal distributions P(x)=1∕(xσ√2π)exp[-(ln(x)-μ)²∕(2σ²)] (distributions truncated at 10−3). Inset: The corresponding distributions of the local maxima of each hashtag trajectory P(Li(tpeak)) (distributions truncated at 103). Source data are provided as a Source Data file and in an online repository (see data availability statement)
Fig. 1 Measuring the speed of hashtag dynamics: a Daily usage Li(t) of the top 50 hashtags i on Twitter in 2016, with exemplary highlighted major events. b Average trajectories 〈L(t − tpeak)〉 leading to a local maximum Li(tpeak) in all top hashtags from 2013 to 2016. The shaded area shows the 95% confidence intervals (CI). In the background a 1% random sample of trajectories is shown in grey. c Temporal density of extreme events (local maxima with a lower threshold for the slopes, for details see Methods section), scattered over 365 days. d Distribution of daily relative losses P(ΔLi^(l)∕Li) and e daily relative gains P(ΔLi^(g)∕Li). The solid lines show fitted log-normal distributions P(x)=1∕(xσ√2π)exp[-(ln(x)-μ)²∕(2σ²)] (distributions truncated at 10−3). Inset: The corresponding distributions of the local maxima of each hashtag trajectory P(Li(tpeak)) (distributions truncated at 103). Source data are provided as a Source Data file and in an online repository (see data availability statement)CC BY · PubMed Central
Fig. 2 Box-plot representation of relative gains in different data sets: The values of the relative gains ΔLi^(g)∕Li, shown in a box-and-whisker representation for a Twitter, b Books, c Movies, d Google, e Reddit, f Publications, and g Wikipedia. The median is shown as a black bar and the mean as a black diamond. Whiskers are chosen to show the 1.5 of the interquartile range. In a the data points are included for illustration, in the other plots they are not shown. The insets show the same representation of the peak values Li(tpeak) over the same time intervals (without mean value). Source data are provided as a Source Data file
Fig. 2 Box-plot representation of relative gains in different data sets: The values of the relative gains ΔLi^(g)∕Li, shown in a box-and-whisker representation for a Twitter, b Books, c Movies, d Google, e Reddit, f Publications, and g Wikipedia. The median is shown as a black bar and the mean as a black diamond. Whiskers are chosen to show the 1.5 of the interquartile range. In a the data points are included for illustration, in the other plots they are not shown. The insets show the same representation of the peak values Li(tpeak) over the same time intervals (without mean value). Source data are provided as a Source Data fileCC BY · PubMed Central
Fig. 3 Modeling attention dynamics: a–c Exemplary details of trajectories resulting from Eq. (1), imitation, saturation and competition. d Total amount of tweets observed per week that contain hashtags among the 50 most commonly used hashtags. Their number grows from two million in late 2013 to four million by the end of 2016. e Average trajectories 〈L(t − tpeak)〉 leading to a local maximum Li(tpeak) from the numerical solution of Eq. (1) with α = 0.005, c = 2.4 and N = 300 and varying rc = rp = r from 9.0 in yellow to 12.0 in dark blue, as indicated in the legend. f Intensity of bursts and timing. The radii of the circles encode the height of the peaks of extreme events. g Distribution of losses from the simulations (lines), compared with the empirical data from Twitter (symbols) and h distribution of gains (distributions truncated at 10−3). The inset shows the overlap of the distributions of peak heights P(Li(tpeak)) (simulated values are rescaled and truncated at 103)
Fig. 3 Modeling attention dynamics: a–c Exemplary details of trajectories resulting from Eq. (1), imitation, saturation and competition. d Total amount of tweets observed per week that contain hashtags among the 50 most commonly used hashtags. Their number grows from two million in late 2013 to four million by the end of 2016. e Average trajectories 〈L(t − tpeak)〉 leading to a local maximum Li(tpeak) from the numerical solution of Eq. (1) with α = 0.005, c = 2.4 and N = 300 and varying rc = rp = r from 9.0 in yellow to 12.0 in dark blue, as indicated in the legend. f Intensity of bursts and timing. The radii of the circles encode the height of the peaks of extreme events. g Distribution of losses from the simulations (lines), compared with the empirical data from Twitter (symbols) and h distribution of gains (distributions truncated at 10−3). The inset shows the overlap of the distributions of peak heights P(Li(tpeak)) (simulated values are rescaled and truncated at 103)CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 38 references

Research works printed but not linked: 4

  1. Salganik, M. J. Bit by Bit: Social Research in the Digital Age (Princeton, NJ: Princeton University Press, 2017).
  2. Simon, H. A. Designing organizations for an information-rich world. In M. Greenberge (Ed.), Computers communications, and the public interest (pp. 37-52) (Baltimore, MD: John Hopkins Press, 1971).
  3. Requested at: https://journals.aps.org/datasets. Accessed July 5, 2018.
  4. Rogers, E. M. Communication Technology, vol. 1 (New York: Simon and Schuster, 1986).

Cited by

Sorted by citations

Items 101–200 of 304

Related

See all

Metadata

Authors
Lorenz-Spreen, P., Mønsted, B. M., Hövel, P., & Lehmann, S.
Affiliations
  • Max Planck Institute for Human Development (DE)
  • Technical University of Denmark (DK)
  • University College Cork (IE)
  • University of Copenhagen (DK)
Corresponding author
  • Hövel, Philipp
  • Lehmann, Sune
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-04-15
Volume
10
Issue
1
Pages
1759-1759
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-019-09311-w
PMC
PMC6465266
OpenAlex
W2939088842
Cited by
304
References
38
Citations by year
2026: 46 · 2025: 51 · 2024: 40 · 2023: 43 · 2022: 45 · 2021: 41 · 2020: 32 · 2019: 6
FWCI
14.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
  • Arrow@dit (Dublin Institute of Technology) (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Cork Open Research Archive (University College Cork, Ireland) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
  • MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
Funders
Deutsche Forschungsgemeinschaft
Topics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
  • Social Sciences › Communication › Digital Media and Philosophy
Keywords
collective attention, attention dynamics, cultural artifacts, popularity, social media
MeSH terms
Attention, Humans, Models, Theoretical, Information Dissemination, Social Media

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review