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2篇 您的检索式：作者名="Brian Spitzberg"

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		题名	作者	年代	出处	被引量
	1	Mapping ideas from cyberspace to realspace: visualizing the spatial context of keywords from web page search results显示文摘We introduce a new method for visualizing and analyzing information landscapes of ideas and events posted on public web pages through customized web-search engines and keywords.This research integrates GIScience and web-search engines to track and analyze public web pages and their web contents with associated spatial relationships.Web pages searched by clusters of keywords were mapped with real-world coordinates(by geolocating their Internet Protocol addresses).The resulting maps represent web information landscapes consisting of hundreds of populated web pages searched by selected keywords.By creating a Spatial Web Automatic Reasoning and Mapping System prototype,researchers can visualize the spread of web pages associated with specific keywords,concepts,ideas,or news over time and space.These maps may reveal important spatial relationships and spatial context associated with selected keywords.This approach may provide a new research direction for geographers to study the diffusion of human thought and ideas.A better understanding of the spatial and temporal dynamics of the‘collective thinking of human beings’over the Internet may help us understand various innovation diffusion processes,human behaviors,and social movements around the world.	Ming-Hsiang Tsou Ick-Hoi Kim Sarah Wandersee Daniel Lusher Li An Brian Spitzberg Dipak Gupta Jean Mark Gawron Jennifer Smith Jiue-An Yang Su Yeon Han	2014	International Journal of Digital Earth2014，7，4：	1
	2	Detecting events from the social media through exemplar-enhanced supervised learning显示文摘Understanding and detecting the intended meaning in social media is challenging because social media messages contain varieties of noise and chaos that are irrelevant to the themes of interests.For example,conventional supervised classification approaches would produce inconsistent solutions to detecting and clarifying whether any given Twitter message is really about a wildfire event.Consequently,a renovated workflow was designed and implemented.The workflow consists of four sequential procedures:(1)Apply the latent semantic analysis and cosine similarity calculation to examine the similarity between Twitter messages;(2)Apply Affinity Propagation to identify exemplars of Twitter messages;(3)Apply the cosine similarity calculation again to automatically match the exemplars to known training results,and(4)Apply accumulative exemplars to classify Twitter messages using a support vector machine approach.The overall correction ratio was over 90%when a series of ongoing and historical wildfire events were examined.	Xuan Shi Bowei Xue Ming-Hsiang Tsou Xinyue Ye Brian Spitzberg Jean Mark Gawron Heather Corliss Jay Lee Ruoming Jin	2019	International Journal of Digital Earth2019，12，9：	0

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