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Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes

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  • Georgiadou, Elena
  • Angelopoulos, Spyros
  • Drake, Helen

Abstract

We introduce Big Data Analytics (BDA) and Sentiment Analysis (SA) to the study of international negotiations, through an application to the case of the UK-EU Brexit negotiations and the use of Twitter user sentiment. We show that SA of tweets has potential as a real-time barometer of public sentiment towards negotiating outcomes to inform government decision-making. Despite the increasing need for information on collective preferences regarding possible negotiating outcomes, negotiators have been slow to capitalise on BDA. Through SA on a corpus of 13,018,367 tweets on defined Brexit hashtags, we illustrate how SA can provide a platform for decision-makers engaged in international negotiations to grasp collective preferences. We show that BDA and SA can enhance decision-making and strategy in public policy and negotiation contexts of the magnitude of Brexit. Our findings indicate that the preferred or least preferred Brexit outcomes could have been inferred by the emotions expressed by Twitter users. We argue that BDA can be a mechanism to map the different options available to decision-makers and bring insights to and inform their decision-making. Our work, thereby, proposes SA as part of the international negotiation toolbox to remedy for the existing informational gap between decision makers and citizens’ preferred outcomes.

Suggested Citation

  • Georgiadou, Elena & Angelopoulos, Spyros & Drake, Helen, 2020. "Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes," International Journal of Information Management, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:ininma:v:51:y:2020:i:c:s0268401219309454
    DOI: 10.1016/j.ijinfomgt.2019.102048
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    Cited by:

    1. Md Altab Hossin & Jie Du & Lei Mu & Isaac Owusu Asante, 2023. "Big Data-Driven Public Policy Decisions: Transformation Toward Smart Governance," SAGE Open, , vol. 13(4), pages 21582440231, December.
    2. Cano-Marin, Enrique & Mora-Cantallops, Marçal & Sánchez-Alonso, Salvador, 2023. "Twitter as a predictive system: A systematic literature review," Journal of Business Research, Elsevier, vol. 157(C).
    3. Meihua Zuo & Spyros Angelopoulos & Zhouyang Liang & Carol X. J. Ou, 2023. "Blazing the Trail: Considering Browsing Path Dependence in Online Service Response Strategy," Information Systems Frontiers, Springer, vol. 25(4), pages 1605-1619, August.

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