Home Location Estimation Using Weather Observation Data

We can extract useful information from social media data by adding the user's home location. However, since the user's home location is generally not publicly available, many researchers have been attempting to develop a more accurate home location estimation. In this study, we propose a method to estimate a Twitter user's home location by using weather observation data from AMeDAS. In our method, we first estimate the weather of the area posted by an estimation target user by using the tweet, Next, we check out the estimated weather against weather observation data, and narrow down the area posted by the user. Finally, the user's home location is estimated as which areas the user frequently posts from. In our experiments, the results indicate that our method functions effectively and also demonstrate that accuracy improves under certain conditions.Comment: The 2017 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA2017

Pilot study on the intertwined effects of attitude, relatedness and social norms on environmental behaviour

This study examined the role of the fulfilment of a social need for relatedness in the Self-Determination Theory as a moderator for the discrepant relationship between attitudes and actual behaviours toward the natural environment. Three hundred sixty-four participants answered an online survey evaluating attitudes, behaviours, and perceived social norms regarding the environment, as well as fulfilment of need for relatedness. Pearson correlational analysis, a univariate analysis of variance, and a series of t-tests on their responses suggest that pro-environmental individuals were more likely to act consistently with their attitudes in a social environment with contrasting views if their need for relatedness is fulfilled. Findings suggest that consideration be given to interpersonal relationships and relatedness need fulfilment in the environmental movement.This work was supported by JSPS KAKENHI [Grant Number JP19K13440]; Middlebury Fund for Faculty Professional Development Fund; and the Middlebury College Senior Work Fund