Reprint
Big Data Research for Social Sciences and Social Impact
Edited by
March 2020
416 pages
- ISBN978-3-03928-220-3 (Paperback)
- ISBN978-3-03928-221-0 (PDF)
This book is a reprint of the Special Issue Big Data Research for Social Sciences and Social Impact that was published in
Business & Economics
Environmental & Earth Sciences
Social Sciences, Arts & Humanities
Summary
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.
Format
- Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
big data; illegal accommodation; institutional innovation; transaction costs; housing problem; building stock management; Hong Kong; information diffusion; community detection; topic analysis; sentiment analysis; social networks; Social network; sustainable development; review voting; online community; paradox; smart citizens; experimental cities; smart cities; technopolitics; big data; Barcelona; data commons; decision-makers; policy; GDPR; big data; social sciences; decision-making; data analyst; filtering; framing; big data; maturity model; temporal analytics; advanced business analytics; big data analytic methods; semantic network analysis; framings; NodeXL; promising technology; research frontier; bibliometric analysis; hype cycle; technology platforms; sustainable agri-food systems; innovation in sustainable agriculture; online data; data mining; TP organics; big data research; point of interests (POI); sustainability development; spatial accessibility of residential public services; Xiamen City; big data; sales prediction; online word-of-mouth; dynamic topic model; product attributes; back-propagation neural network; systematic and replicable patent analysis method; problem-solved concept; context–problem network; network data analysis; sustainable wireless energy transmission technology; big data analytics; text mining; association rule; car review; skills; researchers; early career; text mining; social media big data; lbsn; check-in density; spatiotemporal analysis; KDE; GWR; SDE; Guangzhou; educational data mining; learning analytics; machine learning; big data; prediction grades; destination image; user-generated content; online travel review; big data analytics; opinion mining; sentiment analysis; resource optimisation; place sustainability; TripAdvisor; Greek Attica; opinion mining; social media; social networks; sentiment analysis; sentiment polarity classification; social impact; big data research; information systems; analytics; decision making; social sciences; big data research; social and humanistic computing; social sciences; social good; social impact; machine learning; knowledge management; web science; data science; social inclusive economic growth; sustainability; innovation; innovation networks