Get Human-Centered Social Media Analytics PDF

By Yun Fu

ISBN-10: 3319054902

ISBN-13: 9783319054902

ISBN-10: 3319054910

ISBN-13: 9783319054919

This publication offers a well timed and specified survey of next-generation social computational methodologies. The textual content explains the basics of this box, and describes cutting-edge equipment for inferring social prestige, relationships, personal tastes, intentions, personalities, wishes, and life from human info in unconstrained visible information. themes and lines: contains views from a global and interdisciplinary number of pre-eminent experts; provides balanced insurance of either specific 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 innovations for spotting the social roles performed via 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 wonder, and occupation.

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Gend. Educ. 5(2), 179–189 (1993) 2 Learning Social Relations from Videos: Features, Models, and Analytics 41 20. : A linear programming approach for multiple object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (2007) 21. : Support vector regression for multi-view gait recognition based on local motion feature selection. In: IEEE Conference on Computer Vision and Pattern Recognition (2010) 22. : Physically plausible 3d scene tracking: the single actor hypothesis. In: IEEE Conference on Computer Vision and Pattern Recognition (2013) 23.

In our problem domain, when the communities correspond to two adversarial social groups, their expected leaders relate to the hero or the villain in the video. 1 The movies in our dataset broadly contain two rival communities with a designated leader for each community. 2, the dataset contains visual and auditory features, movie script, and closed caption data, all of which are temporally aligned. For movie domain, in order to align visual and auditory features with the script, we require temporal segmentation of the movie into scenes, which provides start and stop timings for each scene.

In order for computational convenience, the appearance functions of actors are approximated as a scene–actor relation matrix denoted by A = {Ai, j }, where Ai, j = 1 if there exists t ∈ L i , where L i is the temporal interval of si , such that χ j (t) = 1. This, for a movie, can be obtained by searching for speaker names in the script. This representation is reminiscent of the actor–event graph in social network analysis [39]. While the actor relations in A can be directly used for construction of the social network, we will demonstrate later that the use of visual and auditory scene features can lead to a better social network representation.

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Human-Centered Social Media Analytics by Yun Fu


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