000 03689nam a22005415i 4500
001 978-3-319-09117-4
003 DE-He213
005 20200421112219.0
007 cr nn 008mamaa
008 140811s2015 gw | s |||| 0|eng d
020 _a9783319091174
_9978-3-319-09117-4
024 7 _a10.1007/978-3-319-09117-4
_2doi
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
072 7 _aTTBM
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aCOM073000
_2bisacsh
082 0 4 _a621.382
_223
100 1 _aRoy, Suman Deb.
_eauthor.
245 1 0 _aSocial Multimedia Signals
_h[electronic resource] :
_bA Signal Processing Approach to Social Network Phenomena /
_cby Suman Deb Roy, Wenjun Zeng.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aX, 176 p. 95 illus., 79 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aWeb 2.x -- Media on the Web -- The World of Signals -- The Network and the Signal -- Detection - Needle in a Haystack -- Estimation - The Empirical Judgment -- Following Signal Trajectories -- Capturing Cross-Domain Ripples -- Socially-aware Media Applications -- Revelations from Social Multimedia Data -- Socio-Semantic Analysis -- Data Visualization: Gazing at Ripples.
520 _aSocial Multimedia Signals is intended for those whose interest is to study the Social Web and develop automated tools to analyze it better. It is especially useful for researchers experienced with signal processing or multimedia analysis but have little exposure to social networks and social multimedia data. Those new to social multimedia should find the first chapters extremely useful to get a thorough look at how social data behaves. Conversely, social scientists should find useful the authors' introduction to several signal processing techniques that can be employed to manipulate large-scale social data. For those new to signal processing, Chapters 5, 6 and 7 will get readers underway with basic techniques for signal processing from social multimedia. Later chapters include a significant amount of material on machine learning for those interested in intelligent algorithms for the Social Web. The authors wrote this book in a balanced fashion, for multimedia researchers, social scientists, network scientists, data scientists who work with social web data, and professionals who use social media on a daily basis.   �         Explores how media popularity in one domain is determined by another domain; �         Presents a granular look at social networks: micro, meso, and macro; �         Examines finding hidden communities in social networks based on shared multimedia.
650 0 _aEngineering.
650 0 _aIndustrial management.
650 0 _aInput-output equipment (Computers).
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aInput/Output and Data Communications.
650 2 4 _aComputational Intelligence.
650 2 4 _aMedia Management.
700 1 _aZeng, Wenjun.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319091167
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-09117-4
912 _aZDB-2-ENG
942 _cEBK
999 _c57313
_d57313