Arab reactions towards Russo-Ukrainian war

EPJ Data Science

Regular Article

Abdelrahman Ashraf¹  SeifALdin Khaled¹  Abdallah Yahia¹  Moayadeldin Tamer¹  Mohamed A. Khamis²  Walid Gomaa³ 

[1] Egypt Japan University of Science and Technology, P.O. Box 179, 21934, New Borg El-Arab City, Alexandria, Egypt;Egypt Japan University of Science and Technology, P.O. Box 179, 21934, New Borg El-Arab City, Alexandria, Egypt;Ejada Systems Ltd., 620-622 El Horeya Street, Zizinia, Alexandria, Egypt;Egypt Japan University of Science and Technology, P.O. Box 179, 21934, New Borg El-Arab City, Alexandria, Egypt;Faculty of Engineering, Alexandria University, P.O. Box 21544, Alexandria, Egypt;

关键词: Natural language processing;    Arabic text analysis;    Sentiment analysis;    Emotion analysis;    Partiality analysis;    Twitter;    Deep learning;   

DOI  :  10.1140/epjds/s13688-023-00415-4

 received in 2023-04-24, accepted in 2023-08-25,  发布年份 2023

来源: Springer

【 摘 要 】

The aim of this paper is to analyze the Arab peoples reactions and attitudes towards the Russo-Ukraine War through the social media of posted tweets, as a fast means to express opinions. We scrapped over 3 million tweets using some keywords that are related to the war and performed sentiment, emotion, and partiality analyses. For sentiment analysis, we employed a voting technique of several pre-trained Arabic language foundational models. For emotion analysis, we utilized a pre-constructed emotion lexicon. The partiality is analyzed through classifying tweets as being ‘Pro-Russia’, ‘Pro-Ukraine’, or ‘Neither’; and it indicates the bias or empathy towards either of the conflicting parties. This was achieved by constructing a weighted lexicon of n-grams related to either side. We found that the majority of the tweets carried ‘Negative’ sentiment. Emotions were not that obvious with a lot of tweets carrying ‘Mixed Feelings’. The more decisive tweets conveyed either ‘Joy’ or ‘Anger’ emotions. This may be attributed to celebrating victory (‘Joy’) or complaining from destruction (‘Anger’). Finally, for partiality analysis, the amount of tweets classified as being ‘Pro-Ukraine’ was slightly greater than Pro-Russia’ at the beginning of the war (specifically from Feb 2022 till April 2022) then slowly began to decrease until they nearly converged at the start of June 2022 with a shift happening in the empathy towards Russia in August 2022. Our Interpretation for that is with the initial Russian fierce and surprise attack at the beginning and the amount of refugees who escaped to neighboring countries, Ukraine gained much empathy. However, by April 2022, Russian intensity has been decreased and with heavy sanctions the U.S. and West have applied on Russia, Russia has begun to gain such empathy with decrease on the Ukrainian side.

【 授权许可】

CC BY   
© Springer-Verlag GmbH, DE 2023

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