We present a spatial analysis of Bitcoin-accepting merchants using BTC Map,a global crowdsourced dataset built on OpenStreetMap,to provide ground-level evidence on Bitcoin’s payment ecosystem.While prior research emp...We present a spatial analysis of Bitcoin-accepting merchants using BTC Map,a global crowdsourced dataset built on OpenStreetMap,to provide ground-level evidence on Bitcoin’s payment ecosystem.While prior research emphasizes macroeconomic drivers,our analysis of approximately 11,000 merchants shows that local adoption is more strongly shaped by community dynamics and sectoral niches.Acknowledging quality variance in crowdsourced data,we focus on verified regional clusters.We find a global concentration of adoption in the hospitality sector,localised clusters driven by grassroots initiatives rather than national policy and significant presence in alternative healthcare and IT services.These findings highlight the limits of top-down interventions such as El Salvador’s legal tender law and underscore the role of social networks in sustaining adoption.By contrasting spatial micro-level evidence with national studies,this work positions merchant data as a key lens for understanding Bitcoin’s evolving role as a medium of exchange.展开更多
Role–event videos are rich in information but challenging to be understood at the story level.The social roles and behavior patterns of characters largely depend on the interactions among characters and the backgroun...Role–event videos are rich in information but challenging to be understood at the story level.The social roles and behavior patterns of characters largely depend on the interactions among characters and the background events.Understanding them requires analysis of the video contents for a long duration,which is beyond the ability of current algorithms designed for analyzing short-time dynamics.In this paper,we propose In Social Net,an interactive video analytics tool for analyzing the contents of role–event videos.It automatically and dynamically constructs social networks from role–event videos making use of face and expression recognition,and provides a visual interface for interactive analysis of video contents.Together with social network analysis at the back end,In Social Net supports users to investigate characters,their relationships,social roles,factions,and events in the input video.We conduct case studies to demonstrate the effectiveness of In Social Net in assisting the harvest of rich information from role–event videos.We believe the current prototype implementation can be extended to applications beyond movie analysis,e.g.,social psychology experiments to help understand crowd social behaviors.展开更多
基金funded in part by the Rapid Response Fund of the AI for Collective Intelligence(AI4CI)hub,a UKRI National AI Research Hub(grant ref EP/Y028392/1).
文摘We present a spatial analysis of Bitcoin-accepting merchants using BTC Map,a global crowdsourced dataset built on OpenStreetMap,to provide ground-level evidence on Bitcoin’s payment ecosystem.While prior research emphasizes macroeconomic drivers,our analysis of approximately 11,000 merchants shows that local adoption is more strongly shaped by community dynamics and sectoral niches.Acknowledging quality variance in crowdsourced data,we focus on verified regional clusters.We find a global concentration of adoption in the hospitality sector,localised clusters driven by grassroots initiatives rather than national policy and significant presence in alternative healthcare and IT services.These findings highlight the limits of top-down interventions such as El Salvador’s legal tender law and underscore the role of social networks in sustaining adoption.By contrasting spatial micro-level evidence with national studies,this work positions merchant data as a key lens for understanding Bitcoin’s evolving role as a medium of exchange.
基金supported by National Natural Science Foundation of China(No.61802278).
文摘Role–event videos are rich in information but challenging to be understood at the story level.The social roles and behavior patterns of characters largely depend on the interactions among characters and the background events.Understanding them requires analysis of the video contents for a long duration,which is beyond the ability of current algorithms designed for analyzing short-time dynamics.In this paper,we propose In Social Net,an interactive video analytics tool for analyzing the contents of role–event videos.It automatically and dynamically constructs social networks from role–event videos making use of face and expression recognition,and provides a visual interface for interactive analysis of video contents.Together with social network analysis at the back end,In Social Net supports users to investigate characters,their relationships,social roles,factions,and events in the input video.We conduct case studies to demonstrate the effectiveness of In Social Net in assisting the harvest of rich information from role–event videos.We believe the current prototype implementation can be extended to applications beyond movie analysis,e.g.,social psychology experiments to help understand crowd social behaviors.
