Dynamics of online debates: insights from textual network analysis
提出一种聚类共现词网络的方法，优先量化网络随时间变化的相似性，通过张量分解识别各时期主导话题，并用脱欧相关推文验证了该方法在识别社交媒体公共话语模式上的有效性。
Abstract Textual data analysis is critical for monitoring changing themes over time. To overcome challenges posed by data richness, graph theory emerges as a tool for investigating word-topic associations. We present an approach to clustering co-occurrence word networks that prioritises network similarity quantification over time. Addressing theoretical and network geometrical constraints, a statistical framework for manifold data analysis facilitates the grouping of semantic networks, partitioning the observed time frame into periods, and identifying dominant topics in each period via tensor decomposition. The analysis of Brexit-related tweets demonstrates the efficacy of modern methods for identifying social media patterns on public discourse.