Regional Sentiment Bias in Social Media Reporting During Crises

Karin Sim Smith (), Richard McCreadie (), Craig Macdonald () and Iadh Ounis ()
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Karin Sim Smith: University of Glasgow
Richard McCreadie: University of Glasgow
Craig Macdonald: University of Glasgow
Iadh Ounis: University of Glasgow

Information Systems Frontiers, 2018, vol. 20, issue 5, No 9, 1013-1025

Abstract: 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.

Keywords: Social media; Sentiment analysis; Terrorist attacks; Regional bias (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10796-018-9827-x

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