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arXiv · 2208.04903

Characterizing the 2022 Russo-Ukrainian Conflict Through the Lenses of Aspect-Based Sentiment Analysis: Dataset, Methodology, and Preliminary Findings

Maurantonio Caprolu·Alireza Sadighian·Roberto Di Pietro
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Abstract

Online social networks (OSNs) play a crucial role in today's world. On the one hand, they allow free speech, information sharing, and social-movements organization, to cite a few. On the other hand, they are the tool of choice to spread disinformation, hate speech, and to support propaganda. For these reasons, OSNs data mining and analysis aimed at detecting disinformation campaigns that may arm the society and, more in general, poison the democratic posture of states, are essential activities during key events such as elections, pandemics, and conflicts. In this paper, we studied the 2022 Russo-Ukrainian conflict on Twitter, one of the most used OSNs. We quantitatively and qualitatively analyze a dataset of more than 5.5+ million tweets related to the subject, generated by 1.8+ million unique users. By leveraging statistical analysis techniques and aspect-based sentiment analysis (ABSA), we discover hidden insights in the collected data and abnormal patterns in the users' sentiment that in some cases confirm while in other cases disprove common beliefs on the conflict. In particular, based on our findings and contrary to what suggested in some mainstream media, there is no evidence of massive disinformation campaigns. However, we have identified several anomalies in the behavior of particular accounts and in the sentiment trend for some subjects that represent a starting point for further analysis in the field. The adopted techniques, the availability of the data, the replicability of the experiments, and the preliminary findings, other than being interesting on their own, also pave the way to further research in the domain.

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Maurantonio Caprolu, Alireza Sadighian, Roberto Di Pietro. 2022-08-02. Characterizing the 2022 Russo-Ukrainian Conflict Through the Lenses of Aspect-Based Sentiment Analysis: Dataset, Methodology, and Preliminary Findings. https://doi.org/10.1109/icccn58024.2023.10230192

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