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27810 #WhoAmI in 160 characters? Classifying social identities based on Twitter profile descriptions
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Priante, A. and Hiemstra, D. and van den Broek , T. and Saeed, A. and Ehrenhard, M. and Need, A. (2016) #WhoAmI in 160 characters? Classifying social identities based on Twitter profile descriptions. In: Search Proceedings of the First Workshop on NLP and Computational Social Science (BLP+CSS at EMNLP 2016), November 5, 2016, Austin, Texas, USA. pp. 55-65. Association for Computational Linguistics . ISBN 978-1-945626-26-5

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Official URL: http://aclweb.org/anthology/W16-5608

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Abstract

We combine social theory and NLP methods to classify English-speaking Twitter users’ online social identity in profile descriptions. We conduct two text classification experiments. In Experiment 1 we use a 5-category online social identity classification based on identity and self-categorization theories. While we are able to automatically classify two identity categories (Relational and Occupational), automatic classification of the other three identities (Political, Ethnic/religious and Stigmatized) is challenging. In Experiment 2 we test a merger of such identities based on theoretical arguments. We find that by combining these identities we can improve the predictive performance of the classifiers in the experiment. Our study shows how social theory can be used to guide NLP methods, and how such methods provide input to revisit traditional social theory that is strongly consolidated in offline setting

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-DB: Databases, BMS-NIKOS: Centre for Entrepreneurship, Strategy, International Business and Marketing, BMS-PA: Public Administration
Research Program:CTIT-General
Research Project:The Diffusion And Effectiveness of Cancer Early Detection Campaigns on Twitter
ID Code:27810
Status:Published
Deposited On:18 April 2017
Refereed:Yes
International:Yes
More Information:statisticsmetis

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