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Brexit stance annotated tweets

Authors: Grčar, Miha; Cherepnalkoski, Darko; Mozetič, Igor; Kralj Novak, Petra;

Brexit stance annotated tweets

Abstract

The corpus contains over 4.5 million tweets (tweet IDs) automatically labeled by a machine learning program with stance regarding Brexit: Positive (supporting Brexit), Negative (opposing Brexit), or Neutral (uncommitted). The Brexit referendum was held on June 23, 2016, to decide whether the UK should leave or remain in the EU. In the weeks before the referendum, starting on May 12, the UK geo-located Brexit-related tweets were continuously collected resulting in a dataset of around 4.5 million (4,508,440) tweets from almost one million (998,054) users. A large sample of the collected tweets (35,000) was manually labeled for the stance of their authors regarding Brexit: Positive (supporting Brexit), Negative (opposing Brexit), or Neutral (uncommitted). The labeled tweets were used to train a classifier which then automatically labeled all the remaining tweets. The corpus contains tweet ids and stance labels. The tweets are grouped into files one hour per file. In each file, one row represents one entry (twitter_id, sentiment_label). Lines are ordered by the tweet time. The data collection, annotation, model training and performance estimation is described in detail in: Miha Grčar, Darko Cherepnalkoski, Igor Mozetič, Petra Kralj Novak: Stance and influence of Twitter users regarding the Brexit referendum. Computational Social Networks 4/6. 2017. http://dx.doi.org/10.1186/s40649-017-0042-6

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
Average
Average
Average
Funded by
EC| DOLFINS
Project
DOLFINS
Distributed Global Financial Systems for Society
  • Funder: European Commission (EC)
  • Project Code: 640772
  • Funding stream: H2020 | RIA
Related to Research communities
Digital Humanities and Cultural Heritage
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