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Stance and influence of Twitter users regarding the Brexit referendum

Overview of attention for article published in Computational Social Networks, July 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)

Mentioned by

1 blog
13 tweeters


83 Dimensions

Readers on

134 Mendeley
Stance and influence of Twitter users regarding the Brexit referendum
Published in
Computational Social Networks, July 2017
DOI 10.1186/s40649-017-0042-6
Pubmed ID

Miha Grčar, Darko Cherepnalkoski, Igor Mozetič, Petra Kralj Novak


Social media are an important source of information about the political issues, reflecting, as well as influencing, public mood. We present an analysis of Twitter data, collected over 6 weeks before the Brexit referendum, held in the UK in June 2016. We address two questions: what is the relation between the Twitter mood and the referendum outcome, and who were the most influential Twitter users in the pro- and contra-Brexit camps? First, we construct a stance classification model by machine learning methods, and are then able to predict the stance of about one million UK-based Twitter users. The demography of Twitter users is, however, very different from the demography of the voters. By applying a simple age-adjusted mapping to the overall Twitter stance, the results show the prevalence of the pro-Brexit voters, something unexpected by most of the opinion polls. Second, we apply the Hirsch index to estimate the influence, and rank the Twitter users from both camps. We find that the most productive Twitter users are not the most influential, that the pro-Brexit camp was four times more influential, and had considerably larger impact on the campaign than the opponents. Third, we find that the top pro-Brexit communities are considerably more polarized than the contra-Brexit camp. These results show that social media provide a rich resource of data to be exploited, but accumulated knowledge and lessons learned from the opinion polls have to be adapted to the new data sources.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 134 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 28 21%
Student > Ph. D. Student 25 19%
Student > Bachelor 14 10%
Researcher 8 6%
Unspecified 7 5%
Other 22 16%
Unknown 30 22%
Readers by discipline Count As %
Computer Science 34 25%
Social Sciences 27 20%
Unspecified 7 5%
Engineering 5 4%
Business, Management and Accounting 5 4%
Other 23 17%
Unknown 33 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 2022.
All research outputs
of 23,801,276 outputs
Outputs from Computational Social Networks
of 40 outputs
Outputs of similar age
of 317,599 outputs
Outputs of similar age from Computational Social Networks
of 3 outputs
Altmetric has tracked 23,801,276 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 40 research outputs from this source. They receive a mean Attention Score of 3.9. This one scored the same or higher as 38 of them.
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 317,599 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them