First Six Months of War from Ukrainian topic and sentiment analysis
Keywords:information operations, machine learning, topic modelling, sentiment analysis, Ukraine war
Through technological advancements as well as due to societal trends and developments, social media became an active part and a catalysator of the ongoing conflicts and wars carried out in the physical environment. A direct example on this behalf are the cyber/information operations currently conducted in conjunction with the ongoing Russian-Ukrainian war. Due to such operations packaged in social media manipulation mechanisms like disinformation and misinformation using techniques such as controversies, fake news, and deep fakes, a high degree of confusion and uncertainty surrounds the events happened and users’ behaviour and beliefs. These operations also impact the civilians directly affected in the battlefield or their dear and known ones. To tackle this issue, currently limited scientific and objective effort is dedicated in this direction due to, e.g., data, strategic, and emotional implications. It is then the aim of this research to capture the main topics discussed and the feeling expressed by Ukrainian Telegram users on the ongoing Russian-Ukrainian war in 2022 using a Data Science approach by building a series of Machine Learning models based on multi-channel data collected in the first six of months of war. Accordingly, this research directly aims to contribute to efforts on understanding real discourses and dynamics involved in the ongoing conflict through direct resources, producing and sustaining social media security awareness, and building resilience to social media manipulation campaigns using AI.
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