000 | 03636nam a22005415i 4500 | ||
---|---|---|---|
001 | 978-3-319-13590-8 | ||
003 | DE-He213 | ||
005 | 20200420221258.0 | ||
007 | cr nn 008mamaa | ||
008 | 150114s2014 gw | s |||| 0|eng d | ||
020 |
_a9783319135908 _9978-3-319-13590-8 |
||
024 | 7 |
_a10.1007/978-3-319-13590-8 _2doi |
|
050 | 4 | _aQA76.76.A65 | |
072 | 7 |
_aJ _2bicssc |
|
072 | 7 |
_aUB _2bicssc |
|
072 | 7 |
_aCOM018000 _2bisacsh |
|
072 | 7 |
_aSOC000000 _2bisacsh |
|
082 | 0 | 4 |
_a004 _223 |
245 | 1 | 0 |
_aOnline Social Media Analysis and Visualization _h[electronic resource] / _cedited by Jalal Kawash. |
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014. |
|
300 |
_aXVI, 233 p. 94 illus., 76 illus. in color. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
||
347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aLecture Notes in Social Networks, _x2190-5428 |
|
505 | 0 | _aIdentifying Event-Specific Sources from Social Media -- Demographic and Psychographic Estimation of Twitter Users Using Social Structures -- Say It with Colors: Language-Independent Gender Classification on Twitter -- TUCAN: Twitter User Centric Analyzer -- Evaluating Important Factors and Effective Models for Twitter Trend Prediction -- Rings: a Visualization Mechanism to Enhance the User Awareness on Social Networks -- Friends and Circles - A Design Study for Contact Management in Egocentric Online Social Networks -- Genetically Optimized Realistic Social Network Topology Inspired by Facebook -- A Workbench for Visual Design of Executable and Re-usable Network Analysis Workflows -- On the Usage of Network Visualization for Multiagent System Verification. | |
520 | _aThis edited volume addresses the vast challenges of adapting Online Social Media (OSM) to developing research methods and applications. The topics cover generating realistic social network topologies, awareness of user activities, topic and trend generation, estimation of user attributes from their social content, behavior detection, mining social content for common trends, identifying and ranking social content sources, building friend-comprehension tools, and many others. Each of the ten chapters tackle one or more of these issues by proposing new analysis methods or new visualization techniques, or both, for famous OSM applications such as Twitter and Facebook. This collection of contributed chapters address these challenges. Online Social Media has become part of the daily lives of hundreds of millions of users generating an immense amount of 'social content'. Addressing the challenges that stem from this wide adaptation of OSM is what makes this book a valuable contribution to the field of social networks. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aData mining. | |
650 | 0 | _aApplication software. | |
650 | 0 | _aPhysics. | |
650 | 0 | _aStatistics. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aComputer Appl. in Social and Behavioral Sciences. |
650 | 2 | 4 | _aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law. |
650 | 2 | 4 | _aComplex Networks. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
700 | 1 |
_aKawash, Jalal. _eeditor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319135892 |
830 | 0 |
_aLecture Notes in Social Networks, _x2190-5428 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-13590-8 |
912 | _aZDB-2-SCS | ||
942 | _cEBK | ||
999 |
_c53024 _d53024 |