ArticleComputational social networks2017
Stance and influence of Twitter users regarding the Brexit referendum.
Article in Computational social networks, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Finding polarized communities and tracking information diffusion on Twitter: a network approach on the Irish Abortion Referendum.Royal Society open science · 2025Article
- Understanding the complex links between social media and health behaviour.BMJ (Clinical research ed.) · 2024Article
- A Dissemination Model Based on Psychological Theories in Complex Social Networks.IEEE transactions on cognitive and developmental systems · 2022Article
- The Public Perception of the #GeneEditedBabies Event Across Multiple Social Media Platforms: Observational Study.Journal of medical Internet research · 2022Observational
- Social media analytics system for action inspection on social networks.Social network analysis and mining · 2022Article
- Characterizing the role of bots' in polarized stance on social media.Social network analysis and mining · 2022Article
- Retweet communities reveal the main sources of hate speech.PloS one · 2022Article
- Developing insights from the collective voice of target users in Twitter.Journal of big data · 2022Article
- Research using hashtags: A meta-synthesis.Frontiers in sociology · 2022Review
- Veganuary and the vegan sausage (t)rolls: conflict and commercial engagement in online climate-diet discourse.Humanities & social sciences communications · 2022Article
- Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic.Scientific reports · 2021Article
- Community evolution in retweet networks.PloS one · 2021Article
- Networked partisanship and framing: A socio-semantic network analysis of the Italian debate on migration.PloS one · 2021Article
- Moral Expressions in 280 Characters or Less: An Analysis of Politician Tweets Following the 2016 Brexit Referendum Vote.Frontiers in big data · 2021Article
- Evolution of topics and hate speech in retweet network communities.Applied network science · 2021Article
- Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election.PloS one · 2020Article
- Impact investing market on Twitter: influential users and communities.Applied network science · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.