Spatial patterns of the French rail strikes from social networks using weighted <i>k</i>-nearest neighbour

Abstract

The information analysis provided by millions of social network users is one of the most important sources of information yielding interesting insights of spatial patterns about socio-political events. During the recent French National Railway strikes (from April to June, 2018), Twitter was used as platform where people expressed their opinions, with millions of French National Railway Company (SNCF) tweets posted over the strike period. In this paper, we have discussed a methodology which allows the utilisation and interpretation of Twitter data to determine spatial patterns over French territory. The identification of a geographic strike landscape is achieved through spatial interpolation using weighted k-nearest neighbour. This study shows the benefits of geo-statistical learning for extracting sentiment polarities of social events across France.