Spotlight contents extraction from text-based online discussion

Abstract

To understand the development of online discussions and engage effectively, it is a vital issue for both individual participant and facilitator to grasp the contents that the discussion group is focusing, i.e., spotlight contents. However, it becomes extremely challenging to catch up with the spotlight contents in the text-based consensus decision-making online forums (TCDOF) with the increasing of participants and post generation. In this paper, we endeavor to address this challenge through the introduction of a novel framework that leverages topics derived from post contents and inter-post structure to extract spotlight contents from TCDOF. In addition, the extracted spotlight contents are presented in the form of succinct natural language sentences, enhancing accessibility and comprehension. Furthermore, we devise a time-based spotlight contents extraction (TSCE) algorithm to extract spotlight content from a temporal perspective. The effectiveness of the proposed approach is demonstrated with real-world online discussion experiments.

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ORCID for Zhaoxing Li: orcid.org/0000-0003-3560-3461

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