Regional sentiment bias in social media reporting during crises

Smith, K. S., McCreadie, R., Macdonald, C. and Ounis, I. (2018) Regional sentiment bias in social media reporting during crises. Information Systems Frontiers, 50(5), pp. 1013-1025. (doi:10.1007/s10796-018-9827-x)

Smith, K. S., McCreadie, R., Macdonald, C. and Ounis, I. (2018) Regional sentiment bias in social media reporting during crises. Information Systems Frontiers, 50(5), pp. 1013-1025. (doi:10.1007/s10796-018-9827-x)

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Abstract

Crisis events such as terrorist attacks are extensively commented upon on social media platforms such as Twitter. For this reason, social media content posted during emergency events is increasingly being used by news media and in social studies to characterize the public’s reaction to those events. This is typically achieved by having journalists select ‘representative’ tweets to show, or a classifier trained on prior human-annotated tweets is used to provide a sentiment/emotion breakdown for the event. However, social media users, journalists and annotators do not exist in isolation, they each have their own context and world view. In this paper, we ask the question, ‘to what extent do local and international biases affect the sentiments expressed on social media and the way that social media content is interpreted by annotators’. In particular, we perform a multi-lingual study spanning two events and three languages. We show that there are marked disparities between the emotions expressed by users in different languages for an event. For instance, during the 2016 Paris attack, there was 16% more negative comments written in the English than written in French, even though the event originated on French soil. Furthermore, we observed that sentiment biases also affect annotators from those regions, which can negatively impact the accuracy of social media labelling efforts. This highlights the need to consider the sentiment biases of users in different countries, both when analysing events through the lens of social media, but also when using social media as a data source, and for training automatic classification models.

Item Type:Articles
Additional Information:This work was supported by the EC co-funded SUPER (FP7-606853) project.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Ounis, Professor Iadh and Mccreadie, Mr Richard
Authors: Smith, K. S., McCreadie, R., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Information Systems Frontiers
Publisher:Springer
ISSN:1387-3326
ISSN (Online):1572-9419
Published Online:28 February 2018
Copyright Holders:Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature
First Published:First published in Information Systems Frontiers 20(5):1013-1025
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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