Categorizing Memes About the Ukraine Conflict

Keyu Chen; Ashley Feng; Rohan Aanegola; Koustuv Saha; Allie Wong; Zach Schwitzky; Roy Ka Wei Lee; Robin O’Hanlon; Munmun De Choudhury; Frederick L. Altice; Kaveh Khoshnood; Navin Kumar

doi:10.1007/978-3-031-26303-3_3

Categorizing Memes About the Ukraine Conflict

Keyu Chen, Ashley Feng, Rohan Aanegola, Koustuv Saha, Allie Wong, Zach Schwitzky, Roy Ka Wei Lee, Robin O’Hanlon, Munmun De Choudhury, Frederick L. Altice, Kaveh Khoshnood, Navin Kumar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Computational Data and Social Networks - 11th International Conference, CSoNet 2022, Proceedings
Editors	Thang N. Dinh, Minming Li
Publisher	Springer
Pages	27-38
Number of pages	12
ISBN (Print)	9783031263026
DOIs	https://doi.org/10.1007/978-3-031-26303-3_3
State	Published - 2023
Externally published	Yes
Event	11th International Conference on Computational Data and Social Networks, CSoNet 2022 - Virtual, Online Duration: Dec 5 2022 → Dec 7 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13831 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Computational Data and Social Networks, CSoNet 2022
City	Virtual, Online
Period	12/5/22 → 12/7/22

Keywords

Memes
Social media
Ukraine

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Online availability

10.1007/978-3-031-26303-3_3

Library availability

Discover UIUC Full Text

Cite this

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. (2023). Categorizing Memes About the Ukraine Conflict. In T. N. Dinh, & M. Li (Eds.), Computational Data and Social Networks - 11th International Conference, CSoNet 2022, Proceedings (pp. 27-38). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13831 LNCS). Springer. https://doi.org/10.1007/978-3-031-26303-3_3

Categorizing Memes About the Ukraine Conflict. / Chen, Keyu; Feng, Ashley; Aanegola, Rohan et al.
Computational Data and Social Networks - 11th International Conference, CSoNet 2022, Proceedings. ed. / Thang N. Dinh; Minming Li. Springer, 2023. p. 27-38 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13831 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, K, Feng, A, Aanegola, R, Saha, K, Wong, A, Schwitzky, Z, Lee, RKW, O’Hanlon, R, De Choudhury, M, Altice, FL, Khoshnood, K & Kumar, N 2023, Categorizing Memes About the Ukraine Conflict. in TN Dinh & M Li (eds), Computational Data and Social Networks - 11th International Conference, CSoNet 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13831 LNCS, Springer, pp. 27-38, 11th International Conference on Computational Data and Social Networks, CSoNet 2022, Virtual, Online, 12/5/22. https://doi.org/10.1007/978-3-031-26303-3_3

Chen K, Feng A, Aanegola R, Saha K, Wong A, Schwitzky Z et al. Categorizing Memes About the Ukraine Conflict. In Dinh TN, Li M, editors, Computational Data and Social Networks - 11th International Conference, CSoNet 2022, Proceedings. Springer. 2023. p. 27-38. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-26303-3_3

Chen, Keyu ; Feng, Ashley ; Aanegola, Rohan et al. / Categorizing Memes About the Ukraine Conflict. Computational Data and Social Networks - 11th International Conference, CSoNet 2022, Proceedings. editor / Thang N. Dinh ; Minming Li. Springer, 2023. pp. 27-38 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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