It is important to collect and spread accurate information quickly during disasters. Therefore, utilizing Twitter at the time of accidents has been gaining attention in recent year. In this paper, we propose a real-time information sharing system during disaster based on the utilization of Twitter. The proposed system consists of two sub-systems, a disaster information tweeting system that automatically attaches user's current geo-location information (address) and the hashtag of the form “#(municipality name) disaster,” and a disaster information mapping system that displays neighboring disaster-related tweets on a map.

In variable-length coding, the probability of codeword length per source letter being above (resp. below) a prescribed threshold is called the overflow (resp. the underflow) probability. In this paper, we show that the infimum achievable threshold given the overflow probability exponent r always coincides with the infimum achievable fixed-length coding rate given the error exponent r, without any assumptions on the source. In the case of underflow probability, we also show the similar results. From these results, we can utilize various theorems and results on the fixed-length coding established by Han for the analysis of overflow and underflow probabilities. Moreover, we generalize the above results to the case with overflow and underflow probabilities of codeword cost.

The 2019 Typhoon Hagibis (No. 19) caused widespread destruction in eastern Japan. During the disaster, many tweets including rescue request hashtags such as #救助 (meaning #Rescue) and #救助要請 (meaning #Rescue_request) were posted on Twitter. An official disaster information account of the Nagano Prefectural Government asked the public to provide information in the form of damage reports and rescue requests using the hashtag #台風19号長野県被害 (#Typhoon_No.19_Nagano_Prefecture_damage). As a result, many tweets were posted using this hashtag. Moreover, the account contacted the posters of tweets requesting rescue and delivered the information to the Fire Department. In this study, we analyze the circumstances of the above tweets.

The purpose of this paper is to show that a new type of information-theoretic learning method called “potential learning” can be used to detect and extract important tweets among a great number of redundant ones. In the experiment, we used a dataset of 10,000 tweets, among which there existed only a few important ones. The experimental results showed that the new method improved overall classification accuracy by correctly identifying the important tweets.