↓ Skip to main content

Twitter-Based Analysis of the Dynamics of Collective Attention to Political Parties

Overview of attention for article published in PLOS ONE, July 2015
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 (96th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

twitter
84 X users
facebook
2 Facebook pages
googleplus
2 Google+ users

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
92 Mendeley
Title
Twitter-Based Analysis of the Dynamics of Collective Attention to Political Parties
Published in
PLOS ONE, July 2015
DOI 10.1371/journal.pone.0131184
Pubmed ID
Authors

Young-Ho Eom, Michelangelo Puliga, Jasmina Smailović, Igor Mozetič, Guido Caldarelli

Abstract

Large-scale data from social media have a significant potential to describe complex phenomena in the real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, identify the dynamics of the volume, and show that this quantity has some information on the election outcome. We find that the distribution of the tweet volume for each party follows a log-normal distribution with a positive autocorrelation of the volume over short terms, which indicates the volume has large fluctuations of the log-normal distribution yet with a short-term tendency. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion (i.e., the logarithm of the volume moves randomly with a trend). Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting short-term tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media.

X Demographics

X Demographics

The data shown below were collected from the profiles of 84 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 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 2 2%
Germany 1 1%
Malaysia 1 1%
Italy 1 1%
United Kingdom 1 1%
Slovenia 1 1%
Spain 1 1%
United States 1 1%
Unknown 83 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 25%
Student > Master 18 20%
Researcher 17 18%
Student > Bachelor 7 8%
Professor 5 5%
Other 10 11%
Unknown 12 13%
Readers by discipline Count As %
Computer Science 23 25%
Social Sciences 17 18%
Psychology 8 9%
Physics and Astronomy 6 7%
Medicine and Dentistry 5 5%
Other 19 21%
Unknown 14 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 59. 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 22 October 2019.
All research outputs
#729,647
of 25,547,324 outputs
Outputs from PLOS ONE
#9,720
of 222,766 outputs
Outputs of similar age
#8,373
of 277,593 outputs
Outputs of similar age from PLOS ONE
#261
of 6,647 outputs
Altmetric has tracked 25,547,324 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 222,766 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 95% 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 277,593 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 96% of its contemporaries.
We're also able to compare this research output to 6,647 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 96% of its contemporaries.