Online Community Response to Major Disaster: A Study of Tianya Forum in the 2008 Sichuan Earthquake
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
Proceedings of the 2009 International Workshop on Location Based Social Networks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Twitter under crisis: can we trust what we RT?
Proceedings of the First Workshop on Social Media Analytics
Twitter catches the flu: detecting influenza epidemics using Twitter
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Rumor has it: identifying misinformation in microblogs
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Twitcident: fighting fire with information from social web streams
Proceedings of the 21st international conference companion on World Wide Web
Hi-index | 0.00 |
Microblogging systems such as Twitter have become popular. They are especially useful and helpful for users in disaster situations. Microblogs have facilitated the spread of information of all kinds, even rumors. Rumors block adequate information sharing and cause severe problems. Several studies have analyzed rumors, but it remains unclear how rumors are spread on microblogging systems. As described in this paper, we present a case study of how rumors are spread on Twitter in a recent disaster situation, that of the Great East Japan earthquake in March 11 2011, based on comparison to a normal situation. We also specifically examine the correction of rumors because automatic extraction of rumors is difficult, but extracting rumor-correction is easier than extracting the rumors themselves. We (1) classify tweets in disaster situations, (2) analyze tweets in disaster situations based on user's impression, and (3) compare the spread of rumor tweets in a disaster situation to that in a normal situation. A category with only the three required fields