Under the definition of Baseball Stadium as a specific space, locations from which tweets relating to relevant spaces had been posted were classified whether inside or outside the specific space. As the classification method, BERT being one of the natural language processing models was employed. Through the comparison between the features of tweets inside and outside a specific space, it was revealed that there were differences between them in terms the number of URLs and media including photographs provided in them and shown that classification accuracy would improve by combining the numbers of URLs and media provided in tweets and their contents. In addition, through the extraction and comparison of words affecting the results of classification using LIME, what types of words and information had impacts on the judgement of classification were visualized.

This paper aims to estimate the population in a specific space from the numbers of posted tweets and their senders, using Twitter’s real-time property and location information data. The population to be estimated was set to be the attendance at each game among the six baseball teams of the Japan Professional Baseball Pacific League held at the main stadium of each team. The relation between the attendance and Twitter data was analyzed, and random forest regression models using Twitter data were used to estimate the attendances. While there are many studies on event detection or location identification using Twitter data, no study has been reported on the estimation of the population in a specific space using “time information” and “location information” characteristic of Twitter data. Using Twitter data, which contains users’ messages, for estimating the population can be extended to various types of analyses, such as the analysis of feelings and opinions of the groups in the space.

In recent years, head mounted displays (HMDs) have become widely used among general users thanks to the release of low-cost and high-performance versions. It is expected that virtual reality (VR) experiences will often be performed in a relaxed posture, while sitting on a reclining seat at home or in the supine position on a bed in rehabilitation institutions. However, there is no clear knowledge on the subject of how humans perceive the horizontal plane of a virtual world while using HMDs in reclining postures. In this study, we investigated how the angle of the subjective horizontal plane changes in relation to the angle of the upper body in a reclining seat. As a result, we found that the angle of the perceived horizontal plane changes depending on the angle of the upper body, and the physical direction of gravity has little effect on this perception.

We have been investigating about the visual horizontal plane when wearing HMD. We investigated about the change of the visual horizontal plane in the reclining position and the upright position, but there is no clear knowledge as to whether the change in the subjective horizontal plane is due to the change of head angle or the change of posture. In this study, we investigate the effect of head angle on subjective horizontal plane in reclining position and upright position.