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210512 ||| eng |
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|a 9783039282203
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|a 9783039282210
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|a books978-3-03928-221-0
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|a Visvizi, Anna
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|a Big Data Research for Social Sciences and Social Impact
|h Elektronische Ressource
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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|a 1 electronic resource (416 p.)
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|a machine learning
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|a data mining
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|a context-problem network
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|a innovation
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|a skills
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|a sustainable agri-food systems
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|a filtering
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|a social and humanistic computing
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|a social impact
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|a analytics
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|a paradox
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|a lbsn
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|a big data
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|a knowledge management
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|a online data
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|a Social network
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|a innovation in sustainable agriculture
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|a educational data mining
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|a spatial accessibility of residential public services
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|a place sustainability
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|a back-propagation neural network
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|a GDPR
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|a smart cities
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|a bibliometric analysis
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|a social networks
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|a topic analysis
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|a online community
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|a housing problem
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|a web science
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|a technopolitics
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|a promising technology
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|a hype cycle
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|a innovation networks
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|a policy
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|a institutional innovation
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|a big data analytic methods
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|a online word-of-mouth
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|a KDE
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|a Greek Attica
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|a opinion mining
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|a social sciences
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|a GWR
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|a transaction costs
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|a prediction grades
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|a temporal analytics
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|a TripAdvisor
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|a destination image
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|a sales prediction
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|a illegal accommodation
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|a decision making
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|a spatiotemporal analysis
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|a framing
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|a sustainable development
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|a research frontier
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|a product attributes
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|a building stock management
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|a data science
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|a Xiamen City
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|a Hong Kong
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|a information systems
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|a user-generated content
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|a community detection
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|a decision-makers
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|a TP organics
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|a sentiment polarity classification
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|a semantic network analysis
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|a NodeXL
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|a technology platforms
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|a smart citizens
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|a data analyst
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|a History of engineering and technology / bicssc
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|a decision-making
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|a social good
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|a social media
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|a sustainable wireless energy transmission technology
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|a online travel review
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|a text mining
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|a advanced business analytics
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|a network data analysis
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|a problem-solved concept
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|a sustainability development
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|a learning analytics
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|a researchers
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|a Guangzhou
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|a check-in density
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|a sentiment analysis
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|a maturity model
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|a early career
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|a social media big data
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|a point of interests (POI)
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|a Barcelona
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|a resource optimisation
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|a dynamic topic model
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|a data commons
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|a experimental cities
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|a review voting
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|a association rule
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|a SDE
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|a car review
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|a information diffusion
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|a systematic and replicable patent analysis method
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|a sustainability
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|a social inclusive economic growth
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|a big data analytics
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|a big data research
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|a framings
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|a Lytras, Miltiadis
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|a Chui, Kwok Tai
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|a eng
|2 ISO 639-2
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|b DOAB
|a Directory of Open Access Books
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
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|a 10.3390/books978-3-03928-221-0
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|u https://directory.doabooks.org/handle/20.500.12854/42089
|z DOAB: description of the publication
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|u https://www.mdpi.com/books/pdfview/book/2111
|7 0
|x Verlag
|3 Volltext
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|a A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insight to the social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of big data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. The organization of the relevant debate is arranged around three pillars: Section A: Big Data Research for Social Impact: • Big Data and Their Social Impact; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data for Innovation. Section B. Techniques and Methods for Big Data driven research for Social Sciences and Social Impact: • Opinion Mining on Social Media; • Sentiment Analysis of User Preferences; • Sustainable Urban Communities; • Gender Based Check-In Behavior by Using Social Media Big Data; • Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception. Section C. Big Data Research Strategies: • Skill Needs for Early Career Researchers-A Text Mining Approach; • Pattern Recognition through Bibliometric Analysis; • Assessing an Organization's Readiness to Adopt Big Data; • Machine Learning for Predicting Performance; • Analyzing Online Reviews Using Text Mining; • Context-Problem Network and Quantitative Method of Patent Analysis. Complementary social and technological factors including: • Big Social Networks on Sustainable Economic Development; Business Intelligence.
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