Human-Centered Social Media Analytics
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Beschreibung
Utilizing the ubiquity of social media in modern society, the emerging interdisciplinary field of social computing offers the promise of important human-centered applications.
Human-Centered Social Media Analytics provides a timely and unique survey of next-generation social computational methodologies. The text explains the fundamentals of this field, and describes state-of-the-art methods for inferring social status, relationships, preferences, intentions, personalities, needs, and lifestyles from human information in unconstrained visual data. The collected chapters present a range of different viewpoints examining the various possibilities and challenges to machine understanding of humans in a social context.
Topics and features:
- Includes perspectives from an international and interdisciplinary selection of pre-eminent authorities
- Presents balanced coverage of both detailed theoretical analysis and real-world applications
- Examines social relationships in human-centered media for the development of socially-aware video, location-based, and multimedia applications
- Reviews techniques for recognizing the social roles played by people in an event, and for classifying human-object interaction activities
- Discusses the prediction and recognition of human attributes via social media analytics, including social relationships, facial age and beauty, and occupation
- Requires no prior background knowledge of the area
This authoritative text/reference will be a valuable resource for researchers and graduate students interested in social media and networking, computer vision and biometrics, big data, and HCI. Practitioners in these fields, as well as in image processing and computer graphics, will also find the book of great interest.
Dr. Yun Fu is an assistant professor in the Department of Electrical and Computer Engineering at Northeastern University, Boston, MA, USA, where he is the founder of the Synergetic Media Learning (SMILE) Lab.This book provides a timely and unique survey of next-generation social computational methodologies. The text explains the fundamentals of this field, and describes state-of-the-art methods for inferring social status, relationships, preferences, intentions, personalities, needs, and lifestyles from human information in unconstrained visual data. Topics and features: includes perspectives from an international and interdisciplinary selection of pre-eminent authorities; presents balanced coverage of both detailed theoretical analysis and real-world applications; examines social relationships in human-centered media for the development of socially-aware video, location-based, and multimedia applications; reviews techniques for recognizing the social roles played by people in an event, and for classifying human-object interaction activities; discusses the prediction and recognition of human attributes via social media analytics, including social relationships, facial age and beauty, and occupation.
Provides a survey of next-generation social computational methodologies, from fundamentals to state-of-the-art techniques Includes perspectives from an international and interdisciplinary selection of pre-eminent authorities Presents balanced coverage of both detailed theoretical analysis and real-world applications Includes supplementary material: sn.pub/extras
Autor*in
Yun Fu
Themen in »Human-Centered Social Media Analytics«
Age Estimation Big Data Computer Vision Demographic Sensing Face Recognition Human-Centered Computing Human-Computer Interaction Image Processing Kinship Verification Occupation Recognition Pattern Recognition Social Media
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“Human-Centered Social Media Analytics focuses on the novel social computational methodologies that are being developed to investigate social media data. … Scholars, both new and established, should consider reading … to gain an understanding of the questions they should pursue and the challenges they must overcome as they strive to advance big data and social media analytics research.” (Pratyush Bharati, Interfaces, Vol. 47 (3), May-June, 2017)
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