↓ 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, January 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 (97th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

twitter
88 tweeters
facebook
2 Facebook pages
googleplus
2 Google+ users

Citations

dimensions_citation
9 Dimensions

Readers on

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

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

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.

Twitter Demographics

The data shown below were collected from the profiles of 88 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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 2 3%
Spain 1 2%
Germany 1 2%
Slovenia 1 2%
Malaysia 1 2%
United Kingdom 1 2%
Italy 1 2%
United States 1 2%
Unknown 55 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 25%
Researcher 16 25%
Student > Master 13 20%
Professor 5 8%
Lecturer 4 6%
Other 10 16%
Readers by discipline Count As %
Computer Science 19 30%
Social Sciences 13 20%
Psychology 7 11%
Unspecified 5 8%
Physics and Astronomy 5 8%
Other 15 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 63. 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 24 March 2017.
All research outputs
#198,915
of 11,438,935 outputs
Outputs from PLoS ONE
#4,319
of 127,076 outputs
Outputs of similar age
#5,555
of 232,611 outputs
Outputs of similar age from PLoS ONE
#184
of 6,350 outputs
Altmetric has tracked 11,438,935 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 127,076 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. This one has done particularly well, scoring higher than 96% 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 232,611 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 97% of its contemporaries.
We're also able to compare this research output to 6,350 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.