000 | 03272nam a22005895i 4500 | ||
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001 | 978-3-319-07142-8 | ||
003 | DE-He213 | ||
005 | 20200421112546.0 | ||
007 | cr nn 008mamaa | ||
008 | 140724s2014 gw | s |||| 0|eng d | ||
020 |
_a9783319071428 _9978-3-319-07142-8 |
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024 | 7 |
_a10.1007/978-3-319-07142-8 _2doi |
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050 | 4 | _aQA76.76.A65 | |
072 | 7 |
_aJ _2bicssc |
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072 | 7 |
_aUB _2bicssc |
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072 | 7 |
_aCOM018000 _2bisacsh |
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072 | 7 |
_aSOC000000 _2bisacsh |
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082 | 0 | 4 |
_a004 _223 |
245 | 1 | 0 |
_aPredicting Real World Behaviors from Virtual World Data _h[electronic resource] / _cedited by Muhammad Aurangzeb Ahmad, Cuihua Shen, Jaideep Srivastava, Noshir Contractor. |
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014. |
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300 |
_aXIV, 118 p. 40 illus., 27 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSpringer Proceedings in Complexity, _x2213-8684 |
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505 | 0 | _aPreface -- On The Problem of Predicting Real World Characteristics from Virtual Worlds -- The Use of Social Science Methods to Predict Player Characteristics from Avatar Observations -- Analyzing Effects of Public Communication onto Player Behavior in Massively Multiplayer Online Games -- Identifying User Demographic Traits through Virtual-World Language Use -- Predicting MMO Player Gender from In-Game Attributes using Machine Learning Models -- Predicting Links in Human Contact Networks using Online Social Proximity -- Identifying a Typology of Players Based on Longitudinal Game Data. | |
520 | _aThis book addresses prediction, mining and analysis of offline characteristics and behaviors from online data and vice versa. Each chapter will focus on a different aspect of virtual worlds to real world prediction e.g., demographics, personality, location, etc. There is a growing body of literature that focuses on the similarities and differences between how people behave in the offline world vs. how they behave in these virtual environments. Data mining has aided in discovering interesting insights with respect to how people behave in these virtual environments. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aApplication software. | |
650 | 0 | _aMathematics. | |
650 | 0 | _aSocial sciences. | |
650 | 0 | _aSociophysics. | |
650 | 0 | _aEconophysics. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aComputer Appl. in Social and Behavioral Sciences. |
650 | 2 | 4 | _aSocio- and Econophysics, Population and Evolutionary Models. |
650 | 2 | 4 | _aMethodology of the Social Sciences. |
650 | 2 | 4 | _aMathematics in the Humanities and Social Sciences. |
700 | 1 |
_aAhmad, Muhammad Aurangzeb. _eeditor. |
|
700 | 1 |
_aShen, Cuihua. _eeditor. |
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700 | 1 |
_aSrivastava, Jaideep. _eeditor. |
|
700 | 1 |
_aContractor, Noshir. _eeditor. |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319071411 |
830 | 0 |
_aSpringer Proceedings in Complexity, _x2213-8684 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-07142-8 |
912 | _aZDB-2-SCS | ||
942 | _cEBK | ||
999 |
_c58601 _d58601 |