An Unsupervised Graph-Based Approach for Detecting Relevant Topics: A Case Study on the Italian Twitter Cohort during the Russia-Ukraine Conflict

On 24 February 2022, the invasion of Ukraine by Russian troops began, starting a dramatic conflict. As in all modern conflicts, the battlefield is both real and virtual. Social networks have had peaks in use and many scholars have seen a strong risk of disinformation. In this study, through an unsupervised topic tracking system implemented with Natural Language Processing and graph-based techniques framed within a biological metaphor, the Italian social context is analyzed, in particular, by processing data from Twitter (texts and metadata) captured during the first month of the war. The system, improved if compared to previous versions, has proved to be effective in highlighting the emerging topics, all the main events and any links between them.

Automated summary

On February 24, 2022, the Russian invasion of Ukraine initiated a dramatic conflict. The battlefield in this modern conflict exists both in the physical world and in the virtual realm, with social networks playing a significant role. Scholars have expressed concern about the spread of disinformation on these platforms. This study utilizes an unsupervised topic tracking system that combines Natural Language Processing and graph-based techniques to analyze the Italian social context, specifically focusing on Twitter data and metadata captured during the first month of the war. This improved system effectively highlights emerging topics, major events, and their interconnections.