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タイトル
和文: 
英文:Quantifying moral foundations from various topics on Twitter conversations 
著者
和文: R. Kaur, 笹原 和俊.  
英文: R. Kaur, K. Sasahara.  
言語 English 
掲載誌/書名
和文: 
英文:2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) 
巻, 号, ページ         2505-2512
出版年月 2016年 
出版者
和文: 
英文: 
会議名称
和文: 
英文:4th IEEE International Conference on Big Data (Big Data) 
開催地
和文: 
英文:Washington, DC 
公式リンク https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000399115002073&DestApp=WOS_CPL
 
DOI https://doi.org/10.1109/BigData.2016.7840889
アブストラクト Moral foundations theory explains variations in moral behavior using innate moral foundations: Care, Fairness, Ingroup, Authority, and Purity, along with experimental supports. However, little is known about the roles of and relationships between those foundations in everyday moral situations. To address these, we quantify moral foundations from a large amount of online conversations (tweets) about moral topics on the social media site Twitter. We measure moral loadings using latent semantic analysis of tweets related to topics on abortion, homosexuality, immigration, religion, and immorality in general, showing how the five moral foundations function in spontaneous conversations about moral violating situations. The results indicate that although the five foundations are mutually related, Purity is the most distinctive foundation and Care is the most dominant foundation in everyday conversations on immorality. Our study shows a new possibility of natural language processing and social big data for moral psychology.

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