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

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journal contribution
posted on 28.11.2019, 09:04 by Elena GeorgiadouElena Georgiadou, Spyros Angelopoulos, Helen DrakeHelen Drake
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.

Funding

ESRC Brexit Priority Grant (2017, ES/R001847/1), ‘28+ Perspectives on Brexit: a guide to the multi-stakeholder negotiations

History

School

  • Business and Economics
  • Loughborough University London

Department

  • Business

Published in

International Journal of Information Management

Volume

51

Publisher

Elsevier

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

Acceptance date

26/11/2019

Publication date

2019-12-18

Copyright date

2019

ISSN

0268-4012

Language

en

Depositor

Prof Helen Drake. Deposit date: 27 November 2019

Article number

102048