Title: Spatial patterns of the French rail strikes from social networks using weighted k-nearest neighbour

Authors: Rachid Ouaret; Babiga Birregah; Omar Jaafor

Addresses: Department Operational Research and Applied Statistics (ROSAS), University of Technology of Troyes, Troyes, France ' Department Operational Research and Applied Statistics (ROSAS), University of Technology of Troyes, Troyes, France ' Department Operational Research and Applied Statistics (ROSAS), University of Technology of Troyes, Troyes, France

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.

Keywords: weighted k-nearest neighbour; wk-NN; spatial interpolation; Twitter; social networks; railway network; social polarities; sentiments analysis; France.

DOI: 10.1504/IJSNM.2020.105745

International Journal of Social Network Mining, 2020 Vol.3 No.1, pp.52 - 76

Received: 19 Nov 2018
Accepted: 16 Apr 2019
Published online: 11 Mar 2020 *

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