The topic of political polarization has received increased attention for valid reasons. Given that an increased amount of the social exchange for opinions happens online, social media platforms provide a good source of information to investigate various aspects of the phenomena. In this work, data collected from Twitter are used to examine polarization surrounding the topic of the Brexit referendum on the membership of the European Union. The analysis specifically focuses on the question of how different tiers of users in terms of influence can project their opinions and if the polarized conditions affect the relative balance in the broadcast capabilities of the tiers. The results show that during polarization periods, users of the higher tier have increased capabilities to broadcast their information in relation to the lower tiers thereby further dominating the discussion. This validates previous modeling investigations and the hypothesis that polarization provides an opportunity for influencers to increase their relative social capital.
Having accurate building information is paramount for a plethora of applications, including humanitarian efforts, city planning, scientific studies, and navigation systems. While volunteered geographic information from sources such as OpenStreetMap (OSM) has good building geometry coverage, descriptive attributes such as the type of a building are sparse. To fill this gap, this study proposes a supervised learning-based approach to provide meaningful, semantic information for OSM data without manual intervention. We present a basic demonstration of our approach that classifies buildings into either residential or non-residential types for three study areas: Fairfax County in Virginia (VA), Mecklenburg County in North Carolina (NC), and the City of Boulder in Colorado (CO). The model leverages (i) available OSM tags capturing non-spatial attributes, (ii) geometric and topological properties of the building footprints including adjacent types of roads, proximity to parking lots, and building size. The model is trained and tested using ground truth data available for the three study areas. The results show that our approach achieves high accuracy in predicting building types for the selected areas. Additionally, a trained model is transferable with high accuracy to other regions where ground truth data is unavailable. The OSM and data science community are invited to build upon our approach to further enrich the volunteered geographic information in an automated manner.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.