他者評価を利用した食習慣改善ソーシャルメディア  [in Japanese] Social Media to Improve Eating Habits using Others Evaluations  [in Japanese]

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Author(s)

    • 藤井 達也 Fujii Tatsuya
    • 東京大学大学院 情報理工学系研究科 Graduate School of Information Science and Technology, The University of Tokyo
    • 小川 恭平 Ogawa Kyohei
    • 東京大学大学院 情報理工学系研究科 Graduate School of Information Science and Technology, The University of Tokyo
    • 鳴海 拓志 Narumi Takuji
    • 東京大学大学院 情報理工学系研究科 Graduate School of Information Science and Technology, The University of Tokyo
    • 廣瀬 通孝 Hirose Michitaka
    • 東京大学大学院 情報理工学系研究科 Graduate School of Information Science and Technology, The University of Tokyo

Abstract

Modern people are concerned with healthy eating habits; however, sustaining these habits often requires a vigilant self-monitoring and a strong will. The satisfaction found in a meal is influenced not only by the food itself, but also by external stimuli and information. This effect is called expectation assimilation in behavioral science. We propose a social media system that enables people to begin eating meals that are more healthful naturally and without conscious effort. This system uses others' positive evaluations as a trigger of expectation assimilation. Using the proposed system, users share information on their meals and evaluate the yumminess and healthfulness of each other's meals. Novelty of the system is that the system modifies others' evaluations, displaying evaluations of healthfulness as those of yumminess to the user consuming the meal. Therefore, users tend to eat more foods that are evaluated as healthful foods by others and thereby, improve their eating habits without noticing it. In this paper, we report about the mechanism of the proposed system and results of a user study under controlled circumstances. Moreover, we integrated our method with a published mobile application that already had a lot of users. We examined our proposal in the real-world context with the application and, consequently, proved practical effectiveness of the method.

Journal

  • Transactions of the Japanese Society for Artificial Intelligence

    Transactions of the Japanese Society for Artificial Intelligence 30(6), 820-828, 2015

    The Japanese Society for Artificial Intelligence

Codes

  • NII Article ID (NAID)
    130005130280
  • Text Lang
    JPN
  • ISSN
    1346-0714
  • Data Source
    J-STAGE 
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