Our society is built on the benefits of technological development, but this development also entails risks. When the convenience of a new technology outweighs the sense of anxiety about its risks, we can consider that this technology is socially accepted. In this study, we use the social media to analyze the general public's anxiety feelings toward various technologies and model the social acceptability of the new technologies based on this analysis. In this study, 6,452,730 tweets about three technologies: “automated driving,” “electronic currency,” and “drones” were collected, and the social acceptability of the technologies was examined using emotion classification, text categorization, and topic analysis techniques. As a result, we concluded that the anxiety about unemployment can be one perspective for analyzing the social acceptability of a technology.

Nowadays, many user-generated recipe sites are accessible on the internet. On user-generated recipe sites, however, are various spam recipe pages that describe closely similar recipes requiring special cooking equipment, with no preparation explanations. These spam recipes are not useful for users. In fact, they impede user's recipe searches. In this paper, we target closely similar recipes as a first step in extracting spam recipes. If user search results could be classified to identify closely similar recipes, user's recipe searches would be easier and more productive. Clustering tools of many kinds are proposed, but it is difficult to cluster closely similar recipes using only existing clustering tools because recipe sites have a unique page structure comprising a title, ingredients, directions (preparation instructions), and comments. The importance of words from each part differs. We propose a clustering method for user-generated recipe sites based on page structure and important words. Next, we conducted an experiment to measure the benefits of our proposed method. The result of experiment presents the benefits of our proposed method which classify the closely similar recipes.