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Big Data Research for Social Sciences and Social Impact
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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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Keywords
- advanced business analytics
- analytics
- association rule
- back-propagation neural network
- Barcelona
- bibliometric analysis
- big data
- big data analytic methods
- big data analytics
- big data research
- building stock management
- car review
- check-in density
- community detection
- context–problem network
- data analyst
- data commons
- Data mining
- Data science
- Decision making
- decision-makers
- Decision-making
- destination image
- dynamic topic model
- early career
- Educational data mining
- experimental cities
- filtering
- Framing
- framings
- GDPR
- Greek Attica
- Guangzhou
- GWR
- hong kong
- housing problem
- hype cycle
- illegal accommodation
- information diffusion
- Information systems
- innovation
- innovation in sustainable agriculture
- innovation networks
- institutional innovation
- KDE
- Knowledge management
- lbsn
- Learning analytics
- Machine learning
- maturity model
- network data analysis
- NodeXL
- online community
- online data
- online travel review
- online word-of-mouth
- opinion mining
- Paradox
- place sustainability
- point of interests (POI)
- policy
- prediction grades
- problem-solved concept
- product attributes
- promising technology
- research frontier
- researchers
- resource optimisation
- review voting
- sales prediction
- SDE
- semantic network analysis
- sentiment analysis
- sentiment polarity classification
- Skills
- Smart cities
- Smart Citizens
- social and humanistic computing
- social good
- Social impact
- social inclusive economic growth
- Social media
- social media big data
- social network
- Social networks
- Social sciences
- spatial accessibility of residential public services
- spatiotemporal analysis
- sustainability
- sustainability development
- sustainable agri-food systems
- Sustainable development
- sustainable wireless energy transmission technology
- systematic and replicable patent analysis method
- technology platforms
- technopolitics
- temporal analytics
- Text Mining
- topic analysis
- TP organics
- transaction costs
- TripAdvisor
- User-generated content
- web science
- Xiamen City
Links
DOI: 10.3390/books978-3-03928-221-0Editions
