Categorizing memes about the Ukraine conflict Conference Paper


Authors: Chen, K.; Feng, A.; Aanegola, R.; Saha, K.; Wong, A.; Schwitzky, Z.; Lee, R. K. W.; O’Hanlon, R.; De Choudhury, M.; Altice, F. L.; Khoshnood, K.; Kumar, N.
Title: Categorizing memes about the Ukraine conflict
Conference Title: 11th International Conference on Computational Data and Social Network (CSoNet 2022)
Abstract: The Russian disinformation campaign uses pro-Russia memes to polarize Americans, and increase support for the Russian invasion of Ukraine. Thus, it is critical for governments and similar stakeholders to identify pro-Russia memes, countering them with evidence-based information. Identifying broad meme themes is crucial for developing a targeted and strategic counter response. There are also a range of pro-Ukraine memes that bolster support for the Ukrainian cause. As such, we need to identify pro-Ukraine memes and aid with their dissemination to augment global support for Ukraine. We address the indicated issues through the following contributions: 1) Creation of an annotated dataset of pro-Russia (N = 70) and pro-Ukraine (N = 121) memes regarding the Ukraine conflict; 2) Identification of broad themes within the pro-Russia and pro-Ukraine meme categories. Broadly, our findings indicated that pro-Russia memes fall into thematic categories that seek to undermine specific elements of US and their allies’ policy and culture. Pro-Ukraine memes are far more diffuse thematically, highlighting admiration for Ukraine’s people and its leadership. Stakeholders may utilize our findings to develop targeted strategies to mitigate Russian influence operations - possibly reducing effects of the conflict. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords: artificial intelligence; evidence-based; ukraine; social media; social networking (online); annotated datasets; memes; meme; polaris; reducing effects
Journal Title Lecture Notes in Computer Science
Volume: 13831
Conference Dates: 2022 Dec 5-7
Conference Location: Virtual
ISBN: 0302-9743
Publisher: Springer  
Date Published: 2023-01-01
Start Page: 27
End Page: 38
Language: English
DOI: 10.1007/978-3-031-26303-3_3
PROVIDER: scopus
DOI/URL:
Notes: This conference paper was published in a book titled "Computational Data and Social Networks" (ISBN: 978-3-031-26302-6) -- Export Date: 1 May 2023 -- Source: Scopus
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