Convo-Based Attitude Analysis of Twitter Big Data: A Case Study on Ukraine-Russia War Dataset

Abstract

Social media has become a popular platform for studying public perceptions and opinions on important global events like elections, pandemics and international conflicts. Previous studies utilized text mining algorithms to analyze individual messages for references to relevant topics and associated sentiment. Such methods overlook the broader context in which these messages appear and as a result fail to capture often intricate relationships between topics, messages, and their authors. More specifically, these methods do not account for social dynamics among the participants in an online discourse, which typically occurs within a convo (Katsios et al., 2019), a loosely structured cluster of posters interested in a common topic. In this paper, we present a convo-based analysis of a public social media dataset collected over a period of 3 months following the onset of the Ukraine-Russia conflict. In this dataset, we identify the most populous convos, the most influential participants within each, and the topics they discuss. We then demonstrate how the general attitude across these convos shifts over time from a largely pro-Ukraine to an increasingly pro-Russia stance, which we speculate is a result of ongoing influence operations. Our findings provide novel insights into the structure of social media traffic and evolution of attitudes in online populations. This work is a first step towards a more comprehensive framework for social media analysis.

Keywords: big data, social media, attitude detection, attitude shift, Ukraine-Russia

DOI: 10.54941/ahfe1006033