Relationship between emotion and diffusion of disaster information on social media: Case study on 2011 Tohoku earthquake

被引:0
作者
Miura A. [1 ]
Toriumi F. [2 ]
Komori M. [3 ]
Matsumura N. [4 ]
Hiraishi K. [5 ]
机构
[1] Kwansei Gakuin University, Japan
[2] The University of Tokyo, Japan
[3] Osaka Electro-Communication University, Japan
[4] Osaka University, Japan
[5] Keio University, Japan
基金
日本学术振兴会;
关键词
Disaster; Emotion; Information diffusion; Social media; Twitter;
D O I
10.1527/tjsai.NFC-EC1
中图分类号
学科分类号
摘要
In this article, we investigate “retweeting in Twitter” or information transfer behavior in social media to figure out some characteristics of our information processing behavior in emergency situation from social psychological perspective. We made an exploratory log analysis of Twitter focusing on the relationship between diffusion of disaster information and user's emotional response on them. Disaster-related tweets which were retweeted over 10 times around the time of the Great East Japan Earthquake were extracted and emotional words in them were categorized and counted. Frequently retweeted tweets tended to include more negative (anxious or angry) or active emotional words than positive or inactive words. As results of multiple and quantile regression analyses, negative (especially anxious) or active emotional words in tweets had a significant effect on the increase of retweeting regardless of a kind of disasters. The results were discussed in terms of the difference with those based on common tweets. © 2016, Japanese Society for Artificial Intelligence. All rights reserved.
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页数:9
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共 11 条
  • [1] Back M.D., Kufner A.C.P., Egloff B., The emotional timeline of September 11, 2001, Psychological Science, 21, pp. 1417-1419, (2010)
  • [2] Back M.D., Kufner A.C.P., Egloff B., Automatic or the people? Anger on September 11, 2001, and lessons learned for the analysis of large digital data sets, Psychological Science, 22, pp. 837-838, (2011)
  • [3] Berger J., Arousal increases social transmission of information, Psychological Science, 22, pp. 891-893, (2011)
  • [4] Berger J., Milkman K., What makes online content viral?, Journal of Marketing Research, 49, pp. 192-205, (2012)
  • [5] Cohn M.A., Mehl M.R., Pennebaker J.W., Linguistic markers of psychological change surrounding September 11, 2001, Psychological Science, 15, pp. 687-693, (2004)
  • [6] Mendoza M., Poblete B., Castillo C., Twitter under crisis: Can we trust what we RT?, Proceedings of the 1St Workshop on Social Media Analytics (SOMA2010), pp. 71-79, (2010)
  • [7] Miura A., Yamashita K., Psychological and social influences on weblog writing: An online survey of weblog authors in Japan, Journal of Computer-Mediated Communication, 12, pp. 1452-1471, (2007)
  • [8] Miyabe M., Miura A., Aramaki E., Use trend analysis of Twitter after the Great East Japan Earthquake, Proceedings of the 2012 ACM Conference on Computer Supported Cooperative Work (CSCW ‘12), pp. 175-178, (2012)
  • [9] Pennebaker J.W., Francis M.E., Booth R.J., Linguistic Inquiry and Word Count: LIWC, (2001)
  • [10] Russell J.A., A circumplex model of affect, Journal of Personality and Social Psychology, 39, pp. 1161-1178, (1980)