Methods of Extracting Useful Discussion Cases on Social Media for Promoting Novices' Experience

Kota Chiba, Hiroki Nakayama, Ryo Onuma, Hiroaki Kaminaga, Youzou Miyadera, Shoichi Nakamura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Due to the growing importance of skills for proactively finding problems and solutions, many educational institutions provide student-centered exercises such as PBL. In particular, discussion skills are some of the most important for exchanging opinions with other students and summarizing one's own thoughts. For inexperienced students, gaining the experience necessary to understand the flow of discussions and speak precisely is important but difficult. Currently, discussions on social media have become popular, and this is a promising source that should be utilized as a reference for unskilled people. In this research, we aim to develop a mechanism for helping unskilled students gain discussion experience on social media. Specifically, we initially develop methods for extracting from Twitter discussions that have topics similar to a discussion of interest. Moreover, we develop methods for extracting discussion structures from accumulated utterances on the basis of an analysis of utterance-response relationships. On the basis of these methods, we finally develop a system for helping students gain such experience by visualizing discussion cases. In this paper, we mainly describe the methods for extracting cases along with an overview of the support provided. In addition, we describe an experiment using actual tweets and discuss the characteristics of our methods on the basis of the results.

Original languageEnglish
Title of host publication2020 IEEE Conference on Big Data and Analytics, ICBDA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7-11
Number of pages5
ISBN (Electronic)9781728192468
DOIs
Publication statusPublished - 2020 Nov 17
Event2020 IEEE Conference on Big Data and Analytics, ICBDA 2020 - Kota Kinabalu, Malaysia
Duration: 2020 Nov 172020 Nov 19

Publication series

Name2020 IEEE Conference on Big Data and Analytics, ICBDA 2020

Conference

Conference2020 IEEE Conference on Big Data and Analytics, ICBDA 2020
CountryMalaysia
CityKota Kinabalu
Period20/11/1720/11/19

Keywords

  • Discussion Case
  • Discussion Structure
  • Discussion Support
  • Discussion Visualization
  • Sentimental Analysis
  • Social Media

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Modelling and Simulation

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