Visualization of topic transitions in SNSs through document embedding and dimensionality reduction.

Social networking services (SNSs) have become the primary means by which individuals express themselves. Consequently, the thoughts of individuals could be explored by analyzing primary topics on SNSs. In this study, we proposed and developed a novel system for visual analytics to address the following intriguing questions. When do topics change? Do they ever resurface? What do people typically discuss? Using document embedding and dimensionality reduction approaches, we abstracted dynamic topics as several points in a two-dimensional space. In addition, we provided other charts depicting words that appeared at certain moments and their time series dynamics over entire periods. In addition, we created a novel text visualization technique called semantic-preserving word bubbles to visualize words at a specific time. In addition, we demonstrated the efficacy of the proposed system utilizing Twitter data regarding early COVID-19 trends, Fukushima nuclear disaster trends, and user ratings of system usability. In general, we have presented this novel system to aid users in exploring and comprehending the transitions of contents uploaded on SNSs. [ABSTRACT FROM AUTHOR]