At the time of the Great East Japan Earthquake, many Tweets of the disaster had been posted and Twitter had been effectively-utilized as an infrastructure for sharing disaster information and confirming safety. However in Twitter, there have been various kinds of information and also the volume is extremely huge, so a technology to effectively obtain the information on disaster or to filter users depending on their purpose of use are considered essential in order for Twitter to be effective at the time of disaster. Especially some kind of filtering mechanism to easily catch real humans' voices is assumed to be important for getting better performance out of Twitter at the time of disaster. The aim of this study is to numerically-express the characteristics of Twitter users by using the concept of entropy in response to each user's tweeting, replying, and retweeting activities, which are assumed to be the source of Twitter's real time feature, to show the details of Twitter users activities at the time of disaster, and to verify the possibility of this method for automatic user filtering. The real Twitter data distributed around the time of the earthquake is used to analyze, and especially in this paper, the difference of user attributes mainly between bot, cyborg and human is examined by using this data. From the experimental results, the characteristics of Twitter users were clarified with multidimensional quantitative values. The experimental results also showed the possibility for automatic user filtering.