In recent years, flaming on social media has been a problem. To avoid flaming, it is useful for the system to automatically check the sentences whether they include the expressions that are likely to trigger flaming or not before posting messages. In this research, we target two harmful expressions. There are insulting expressions and the expressions that are likely to cause a quarrel. Firstly, we constructed a harmful expression dictionary. Because a large cost requires to collect the expressions manually, we constructed the dictionary semi-automatically by using word distributed representations. The proposed method used distributed representations of the harmful expressions and general expressions as features, and constructed a classifier of harmful/general based on those features. An evaluation experiment found that the proposed method could extract harmful expressions with accuracy of approx. 70%. On the other hand, it was found that the proposed method could also extract unknown expressions, however, it wrongly extracted nonharmful expressions at a rate of approx. 40%.

This goal of our study is to encourage novice students to understand the research field. We propose a thesis information visualization interface to support research reorganization for novice students. Novice Students don't have knowledge and experience of research activities. So novice beginners cannot make good use of the survey. Therefore, in this paper, we will visualize the relation of surveyed paper information. We use Euler diagram for visualization. By experiments, novice students were able to grasp information surveyed by themselves. And it was confirmed to promote understanding of the research field.

Recently, during natural disasters like, earthquakes, typhoons, flood, and heavy snowfall, people actively post messages that mention situations people are facing through social media sites. Therefore, the enhancement of situation awareness in the real world using social data is one of the most attractive research topics. In our previous work, we developed a density-based spatiotemporal analysis system to identify topic-related areas in which there are a huge number of geo-tagged tweets related to a topic are posted. In this paper, we propose a novel density-based spatiotemporal analysis system with a photo image classifier using the pre-trained deep network in order to enhance situation awareness by showing accurate topic-related photos. The photo image classifier using a support vector machine (SVM) based on the pre-trained deep network is integrated into the conventional density-based spatiotemporal analysis system. To evaluate the proposed system, we used actual tweet data sets related to weather topics, "heavy rain" and "heavy snow," in Japan. The experimental results showed that the proposed system can classify photo images related to these weather topics more sensitively compared with our previous method.

Analogy can be effective means to make things easy to understand to others, as it can be seen well by comparing things do not know in known things. However, opportunities and methods of training of explanation ability by using analogical thinking do not exist generally. In this study, for the purpose of training of explanation ability in knowledge transfer by using analogical thinking, we propose a system to encourage understanding of the analogy process and the practice due to repeated. We evaluated to verify the effect of the proposed system.

The purpose of this research is to extract comments on infectious diseases from Twitter and to create an infection map.As a method,we check the facts of the extracted utterance,extract the past tweets of the infected person from the result,and estimate the place where the person lives.We used SVMs both for the fact check and place estimation.

The goal of our study is to support a user's analysis of time-series data in an exploratory manner. Such exploratory analysis requires repeated access to various types of information related to the user's interests such as texts and numerical data. To support such the user's analysis, we have proposed a system that visualizes temporal changes in time-series data and presents the causes of those changes with the data. In this paper, we improve the system by (1) strengthening the linkage between graphs and articles (2) adding graph interactions. In addition, we conduct user experiments and examine the point of arrival of the system and the direction of future expansion.

The aim of this research is to deepen a user's understanding of topics appeared in a series of news articles by having the user grasp the relationship between the articles about the theme they are interested in. In recent years, the number of people using Web news sites is increasing. However, news articles on the Web are diverse and enormous, it is difficult for people without expertise to grasp the relationship between articles at present. To solve the problem, We proposes a system that categorizes the articles based on the relevance of topics apeared in the news articles, reflects them in the hierarchical structure, and visualizes the relationships of the articles clearly. This paper describes the prototype of the system and verifies it.