Abstract
The fake news that circulated on social media in 2020 and 2022 affected the voters of the South Korean general and presidential elections, respectively. The political controversy, centered on the election fraud that occurred in both the elections, received significant attention from the society. This study emphasizes the Twitter discourse and compares the formation and distribution of election fraud. We conduct semantic social network analysis and structural topic modeling (STM) to represent topics and relationships among the emerging themes using Twitter texts related to both elections. Results demonstrate that discourses on the same political issue exhibited unique contents and structures, as information is formed and distributed differently depending on the type of election in South Korea. Furthermore, this study illustrates the process and analysis of large-scale text data collected from Twitter. It also includes new methods, such as political ideology, for considering an analytical dimension.
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