Mapping and Exploring the Dynamics of Inequality Narratives Through Social Media

In this work, we apply a hybrid-AI framework to analyse online discourse about inequality on Twitter. Our approach integrates knowledge from natural language understanding, knowledge graph-based semantics, and network analysis to identify narratives around inequality and analyse their changes during the COVID pandemic. We present a two-step approach: A bird’s-eye perspective on the overall discourse network around inequality maps how the entities, concepts, and events of the narratives are connected by linking these to existing knowledge graphs and can be filtered on tweet metadata such as time. Two connected entities can then be explored in more detail in a fine-grained analysis of how the entities and their relationship are characterised by social media users, highlighting the diffusion of different perspectives on a given topic related to inequality, such as gender, education, and poverty.