Short Review of Intention Mining in Social Crisis Management through Automatic Technologies

Abstract

In the current social environment, social crisis events occur frequently with significant impacts. Group intention mining through automatic technologies for managing social crises has gained extensive attention. This paper presents an overview of research on group intention mining in social crisis events, covering three areas: knowledge graph inference, intention attribution, and risk management. Knowledge graph inference facilitates the detection of group intention in crisis events. It is supported by the construction of crisis knowledge graphs, which organize crisis elements and inter-element relations into structured semantic knowledge. The interpretable semantics in the crisis knowledge graphs enables attribution of intention. Group intention mining consists of intention detection and intention attribution, serving the risk management of social crisis events. To gain insights into the process of group intention mining in social crises, the Covid-19 event is selected as a case study. Finally, the paper proposes future research directions to solve the limitations of existing intention mining methods in social crises.