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Content Sharing in Conflictual Ad-Hoc Twitter Discussions: National Patterns or Universal Trends? Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Detecting Interethnic Relations with the Data from Social Media

Authors : Olessia Koltsova, Sergey Nikolenko, Svetlana Alexeeva, Oleg Nagornyy, Sergei Koltcov

Published in: Digital Transformation and Global Society

Publisher: Springer International Publishing

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Abstract

The ability of social media to rapidly disseminate judgements on ethnicity and to influence offline ethnic relations creates demand for the methods of automatic monitoring of ethnicity related online content. In this study we seek to measure the overall volume of ethnicity related discussion in the Russian language social media and to develop an approach that would automatically detect various aspects of attitudes to those ethnic groups. We develop a comprehensive list of ethnonyms and related bigrams that embrace 97 Post-Soviet ethnic groups and obtain all messages containing one of those words from a two-year period from all Russian language social media (N = 2,660,222 texts). We hand-code 7,181 messages where rare ethnicities are overrepresented and train a number of classifiers to recognize different aspects of authors’ attitudes and other text features. After calculating a number of standard quality metrics, we find that we reach good quality in detecting intergroup conflict, positive intergroup contact, and overall negative and positive sentiment. Relevance to the topic of ethnicity and general attitude to an ethnic group are least well predicted, while some aspects such as calls for violence against an ethnic group are not sufficiently present in the data to be predicted.

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Metadata
Title
Detecting Interethnic Relations with the Data from Social Media
Authors
Olessia Koltsova
Sergey Nikolenko
Svetlana Alexeeva
Oleg Nagornyy
Sergei Koltcov
Copyright Year
2017
Book
Digital Transformation and Global Society

Print ISBN: 978-3-319-69783-3

Electronic ISBN: 978-3-319-69784-0

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-69784-0_2

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