TY - GEN

T1 - Detecting Interethnic Relations with the Data from Social Media

AU - Koltsova, Olessia

AU - Nikolenko, Sergey

AU - Alexeeva, Svetlana

AU - Nagornyy, Oleg

AU - Koltcov, Sergei

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

KW - Classification

KW - Ethnic attitudes

KW - Interethnic relations

KW - Lexicon

KW - Mapping

KW - Social media

UR - http://www.scopus.com/inward/record.url?scp=85034428351&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-69784-0_2

DO - 10.1007/978-3-319-69784-0_2

M3 - Conference contribution

AN - SCOPUS:85034428351

SN - 9783319697833

T3 - Communications in Computer and Information Science

SP - 16

EP - 30

BT - Digital Transformation and Global Society

PB - Springer Nature

T2 - 2nd International Conference on Digital Transformation and Global Society (DTGS)

Y2 - 21 June 2017 through 23 June 2017

ER -