Analyzing Disproportionate Reaction via Comparative Multilingual Targeted Sentiment in Twitter

Smith, K. S., McCreadie, R. , Macdonald, C. and Ounis, I. (2017) Analyzing Disproportionate Reaction via Comparative Multilingual Targeted Sentiment in Twitter. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM '17), Sydney, Australia, 31 Jul - 03 Aug 2017, pp. 317-320. ISBN 9781450349932 (doi:10.1145/3110025.3110066)

Smith, K. S., McCreadie, R. , Macdonald, C. and Ounis, I. (2017) Analyzing Disproportionate Reaction via Comparative Multilingual Targeted Sentiment in Twitter. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM '17), Sydney, Australia, 31 Jul - 03 Aug 2017, pp. 317-320. ISBN 9781450349932 (doi:10.1145/3110025.3110066)

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Abstract

Global events such as terrorist attacks are commented upon in social media, such as Twitter, in different languages and from different parts of the world. Most prior studies have focused on monolingual sentiment analysis, and therefore excluded an extensive proportion of the Twitter userbase. In this paper, we perform a multilingual comparative sentiment analysis study on the terrorist attack in Paris, during November 2015. In particular, we look at targeted sentiment, investigating opinions on specific entities, not simply the general sentiment of each tweet. Given the potentially inflammatory and polarizing effect that these types of tweets may have on attitudes, we examine the sentiments expressed about different targets and explore whether disproportionate reaction was expressed about such targets across different languages. Specifically, we assess whether the sentiment for French speaking Twitter users during the Paris attack differs from English-speaking ones. We identify disproportionately negative attitudes in the English dataset over the French one towards some entities and, via a crowdsourcing experiment, illustrate that this also extends to forming an annotator bias.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Mccreadie, Dr Richard and Ounis, Professor Iadh and Macdonald, Dr Craig
Authors: Smith, K. S., McCreadie, R., Macdonald, C., and Ounis, I.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Research Group:Information Retrieval
ISBN:9781450349932
Copyright Holders:Copyright © 2017 Association for Computing Machinery
First Published:First published in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM '17): 317-320
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
624701SUPERIadh OunisEuropean Commission (EC)606853COM - COMPUTING SCIENCE