By Yun Fu
This booklet presents a well timed and certain survey of next-generation social computational methodologies. The textual content explains the basics of this box, and describes state of the art tools for inferring social prestige, relationships, personal tastes, intentions, personalities, wishes, and existence from human details in unconstrained visible info. themes and lines: contains views from a world and interdisciplinary number of pre-eminent professionals; offers balanced assurance of either certain theoretical research and real-world functions; examines social relationships in human-centered media for the advance of socially-aware video, location-based, and multimedia purposes; reports options for spotting the social roles performed by means of humans in an occasion, and for classifying human-object interplay actions; discusses the prediction and popularity of human attributes through social media analytics, together with social relationships, facial age and sweetness, and occupation.
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Additional resources for Human-Centered Social Media Analytics
That is, we let f i = +1 if u i ∇ 0, and f i = −1 if otherwise. Once the communities in the video are extracted, their leaders are detected by analyzing the centrality of each actor in the community. In the sociology literature, the centrality of an actor is traditionally defined by its degree or betweenness . In this chapter, we, rather, adopt a new technique which is referred to as the eigen-centrality  due to its relation to proposed community detection approach. Let the centrality score, xi for the ith actor be proportional to the sum of the scores of all vertices which are connected to it: xi = λ1 Nj=1 K i,∗ j x j , where N is the total number of actors in the video and λ is a constant.
Nat. Acad. Sci. 106(36), 15274–15278 (2009) 13. : Recognizing action at a distance. In: IEEE International Conference on Computer Vision (2003) 14. : Learning continuous-time social network dynamics. In: Conference on Uncertainty in Artificial Intelligence (2009) 15. : Social interactions: a first-person perspective. In: IEEE Conference on Computer Vision and Pattern Recognition (2012) 16. : Action recognition by learning mid-level motion features. In: IEEE Conference on Computer Vision and Pattern Recognition (2008) 17.
Monitoring, recognizing and discovering social networks. In: IEEE Conference on Computer Vision and Pattern Recognition (2009) 46. : Video scene segmentation using markov chain monte carlo. IEEE Trans. Multimedia 8(4), 686–697 (2006) 47. : Modeling individual and group actions in meetings with layered hmms. IEEE Trans. 1 Background The past decade has seen a rapid development and change of the Web and Internet and we are currently witnessing an explosive growth in the social Web, where large amounts of social media contents are being created constantly by a globally distributed array of disparate sensors including human-sensors, mobile phones, video cameras, etc.
Human-Centered Social Media Analytics by Yun Fu