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Brexit Gold Standard

This repository contains the Brexit Twitter Gold Standard produced by the SSIX Project.

SSIX



Preparation

You will need to prepare a few basic things:

  1. Clone this repository git clone https://bitbucket.org/ssix-project/brexit-gold-standard.git ssix-brexit-gold-standard
  2. Move into the cloned repository: cd ssix-brexit-gold-standard/
  3. You need to have both a python interpreter and pip installed
  4. Install required dependecies: pip install -r requirements.txt --upgrade
  5. Create a new Twitter App, then create a read-only Access Token, and fill in the missing details at brexit-gs.yml

Rebuild full sample

The published sample is anonymized to not distribute any personal detail.

To rebuild the full dataset, you need to execute the following script:

python rebuild.py brexit-sample-20160506-annotated.json

This process could take a while, depending on the pause configured at brexit-gs.yml to obey the Twitter API rate limits.

At the end it willl create a brexit-sample-20160506-annotated-full.json with the full dataset rebuilt, including both the original tweet data and annotations.

Annotations

Each Tweet is annotated with the following information:

  1. sentiment: one of "stay", "leave", "undecided", "no sentiment/don't care", "irrelevant", or "" (left blank).
  2. strength: for “stay” or “leave”: an integer between 1 (very weak) and 5 (very strong) expressing the strength of the opinion. For all other tweets, 0.
  3. context: 1 if the interpretation of the tweet depends on external context, such as a linked article or image, 0 otherwise.

References

Manuela Hürlimann, Keith Cortis, André Freitas, Sergio Fernández, Siegfried Handschuh, and Brian Davis. "A Twitter Sentiment Gold Standard for the Brexit Referendum.". In Proceedings of SEMANTiCS 2016, Leipzig (Germany), Sep 12-15, 2016.

@inproceedings{huerlimann2016twitter,
  title={A Twitter Sentiment Gold Standard for the Brexit Referendum.},
  author={H{\"u}rlimann, Manuela and Cortis, Keith and Freitas, Andr{\'e} and Fern{\'a}ndez, Sergio and Handschuh, Siegfried and Davis, Brian},
  booktitle={12th International Conference on Semantic Systems Proceedings},
  year={2016}
}

Brian Davis, Keith Cortis, Laurentiu Vasiliu, Adamantios Koumpis, Ross McDermott, and Siegfried Handschuh. "Social sentiment indices powered by X-scores.". In 2nd International Conference on Big Data, Small Data, Linked Data and Open Data (ALLDATA 2016). More information, Google Scholar, PDF.

@inproceedings{davis2016ssix,
  title={Social Sentiment Indices Powered by X-Scores},
  author={Davis, Brian and Cortis, Keith and Vasiliu, Laurentiu and Koumpis, Adamantios and McDermott, Ross and Handschuh, Siegfried},
  booktitle={ALLDATA 2016, The Second International Conference on Big Data, Small Data, Linked Data and Open Data},
  pages={12--17},
  year={2016},
  organization={IARIA}
}

Licenses

Software

The software is available under the business-friendly license Apache License, Version 2.0. Therefore, you are completely free to use the software for any purpose, to distribute it, to modify it, and to distribute modified versions of the software, including closed-source, under the terms of the license, without concern for royalties.

Dataset

All of the data from Twitter (tweets, creation dates, tweet ids) are covered by Twitter’s Terms of Service.

The annotations are licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

Please cite [Hürlimann et al. 2016] in all publications using this dataset (see References).

Contact

If you have any questions about this repository, please contact Sergio Fernández. For questions about the data set and annotations, get in touch with Manuela Hürlimann. For any other general question of the SSIX Project, you are welcome to contact us.

Acknowledgements

This work is in part funded by the SSIX Horizon 2020 project (grant agreement No 645425) and Science Foundation Ireland (SFI) (grant number SFI/12/RC/2289).

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