Portraying Collective Spatial Attention in Twitter

Émilien Antoine 1 Adam Jatowt 2 Shoko Wakamiya 1 Yukiko Kawai 1 Toyokazu Akiyama 1
1 Kyoto Sangyo University
2 Kyoto University [Kyoto]
Abstract : Microblogging platforms such as Twitter have been recently frequently used for detecting real-time events. The spatial component, as reflected by user location, usually plays a key role in such systems. However, an often neglected source of spatial information are location mentions expressed in tweet contents. In this paper we demonstrate a novel visualization system for analyzing how Twitter users collectively talk about space and for uncovering correlations between geographical locations of Twitter users and the locations they tweet about. Our exploratory analysis is based on the development of a model of spatial information extraction and representation that allows building effective visual analytics framework for large scale datasets. We show visualization results based on half a year long dataset of Japanese tweets and a four months long collection of tweets from USA. The proposed system allows observing many space related aspects of tweet messages including the average scope of spatial attention of social media users and variances in spatial interest over time. The analytical framework we provide and the findings we outline can be valuable for scientists from diverse research areas and for any users interested in geographical and social aspects of shared online data.
Keywords : Location Mention Twitter Social Network Spatial Analysis Visualization
Type de document :
Communication dans un congrès
SIGKDD ACM Special Interest Group on Knowledge Discovery in Data, Aug 2015, Sydney, Australia. pp.10, 2015, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 〈10.1145/2783258.2783418〉
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Dernière modification le : vendredi 19 janvier 2018 - 15:42:17
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Émilien Antoine, Adam Jatowt, Shoko Wakamiya, Yukiko Kawai, Toyokazu Akiyama. Portraying Collective Spatial Attention in Twitter. SIGKDD ACM Special Interest Group on Knowledge Discovery in Data, Aug 2015, Sydney, Australia. pp.10, 2015, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 〈10.1145/2783258.2783418〉. 〈hal-01195697〉

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