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Abstract
Over the last years, the prodigious success of online social media sites has marked a shift in the way people connect and share information. Coincident with this trend is the proliferation of location-aware devices and the consequent emergence of user-generated geospatial data. From a social scientific perspective, these location data are of incredible value as it can be mined to provide researchers with useful information about activities and opinions across time and space. However, the utilization of geo-located data is a challenging task, both in terms of data management and in terms of knowledge production, which requires a holistic approach. In this paper, we implement an integrated knowledge discovery in cyberspace framework for retrieving, processing and interpreting Twitter geolocated data for the discovery and classification of the latent opinion in user-generated debates on the internet. Text mining techniques, supervised machine learning algorithms and a cluster spatial detection technique are the building blocks of our research framework. As real-word example, we focus on Twitter conversations about Brexit, posted on Uk during the 13 months before the Brexit day. The experimental results, based on various analysis of Brexit-related tweets, demonstrate that different spatial patterns can be identified, clearly distinguishing pro- and anti-Brexit enclaves and delineating interesting Brexit geographies.
Highlights
Nowadays, location-aware mobile devices are prevalent access points to social media services, and several social networking websites allow their users to share their location along with social media posts, either by explicitly specifying the geographical location or by embedding the spatial coordinates in their posts
logistic regression (LR) and support vector machines (SVM) were trained setting the class weight as balanced to mitigate the unbalanced nature of the training dataset; decision tree (DT) and Gaussian naive Bayes (GNB) were run with default parameters
With the proliferation of location aware devices, a large proportion of usergenerated content contributed through social media sites is geolocated, fostering the emergence of geosocial media
Summary
Location-aware mobile devices are prevalent access points to social media services, and several social networking websites allow their users to share their location along with social media posts, either by explicitly specifying the geographical location or by embedding the spatial coordinates (i.e. latitude and longitude) in their posts. To analyse the interdependent relationships among places, time, and contents shared on social media, Tsou and Leitner [55] introduced a new research framework, called knowledge discovery in cyberspace (KDC), which extracts information from geo-located social media data by using highly scalable mining and machine learning algorithms, computational linguistics, geographic information systems, visualisation tools, and spatial statistical methods. As for opinion mining, Twitter offers a valid support to monitor and evaluate people’s views and belief on political phenomena and stands on referendums and elections [6, 18, 23, 56] In this context, the analysis of the geographical dimension of the online political debate provides a picture of attitude and stance territorial heterogeneity and helps in exploring spatial patterns. Spatial statistics techniques can be exploited to identify regions of space where the phenomenon shows distinct features and to detect territorial clusters
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