Grasping Users' Awareness for Environments from their SNS Posts

• Item type: Journal(1)
• 公開日: 2022-03-15
• タイトル: Grasping Users' Awareness for Environments from their SNS Posts
• タイトル / 言語: en
• タイトル: Grasping Users' Awareness for Environments from their SNS Posts
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:若手研究者] overtourism, ecotourism, prediction on eco-friendly users, environmental awareness, social network services
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Kyushu University
• 著者所属: Deloitte Tohmatsu Consulting LLC
• 著者所属: Seoul National University
• 著者所属: Fukuoka University
• 著者所属: Kyushu University
• 著者所属(英): en
• 著者所属(英): Kyushu University
• 著者所属(英): en
• 著者所属(英): Deloitte Tohmatsu Consulting LLC
• 著者所属(英): en
• 著者所属(英): Seoul National University
• 著者所属(英): en
• 著者所属(英): Fukuoka University
• 著者所属(英): en
• 著者所属(英): Kyushu University
• 著者名: Tokinori, Suzuki; Naoto, Kashiwagi; Jounghun, Lee; Kun, Qian; Daisuke, Ikeda
• 著者名(英): Tokinori, Suzuki; Naoto, Kashiwagi; Jounghun, Lee; Kun, Qian; Daisuke, Ikeda
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Overtourism has a negative impact on tourist sites all over the world. Serious problems are environmental issues, such as littering, caused by the rush of too many visitors. It is important to change people's mindset to be more environmentally aware for improving this situation. In particular, if we can find people with a high awareness about environments, we can work effectively to promote eco-friendly behavior by taking them as the start. However, grasping individual awareness is inherently difficult. For this challenge, we utilize SNS data, which are available in large volume, with a hypothesis that people's subconsciousness influences their posts. In this paper, we address two research topics for grasping such awareness. First, we propose a classification task, in which a system is given users' SNS posts about tourist sites, and classifies them into types of their focuses. Experimental results show widely-used classifiers can solve the task at about 0.84 of accuracy using our created dataset. Second, we investigate the relation of the focuses and such awareness with a questionnaire survey targeting over 2,700 people, and show that users' awareness influences focuses of SNS posts with both of a statistical analysis and an analysis using real-world data.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.190; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Overtourism has a negative impact on tourist sites all over the world. Serious problems are environmental issues, such as littering, caused by the rush of too many visitors. It is important to change people's mindset to be more environmentally aware for improving this situation. In particular, if we can find people with a high awareness about environments, we can work effectively to promote eco-friendly behavior by taking them as the start. However, grasping individual awareness is inherently difficult. For this challenge, we utilize SNS data, which are available in large volume, with a hypothesis that people's subconsciousness influences their posts. In this paper, we address two research topics for grasping such awareness. First, we propose a classification task, in which a system is given users' SNS posts about tourist sites, and classifies them into types of their focuses. Experimental results show widely-used classifiers can solve the task at about 0.84 of accuracy using our created dataset. Second, we investigate the relation of the focuses and such awareness with a questionnaire survey targeting over 2,700 people, and show that users' awareness influences focuses of SNS posts with both of a statistical analysis and an analysis using real-world data.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.190; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 63, 号 3, 発行日 2022-03-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764