LEADER 03996nam 22006855 450 001 996418439103316 005 20200630191415.0 010 $a3-030-33698-0 024 7 $a10.1007/978-3-030-33698-1 035 $a(CKB)5280000000190094 035 $a(MiAaPQ)EBC6001936 035 $a(DE-He213)978-3-030-33698-1 035 $a(PPN)242819249 035 $a(EXLCZ)995280000000190094 100 $a20191227d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPutting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation$b[electronic resource] /$fedited by Mehmet Kaya, ?uayip Birinci, Jalal Kawash, Reda Alhajj 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (245 pages) 225 1 $aLecture Notes in Social Networks,$x2190-5428 311 $a3-030-33697-2 330 $aThis book focusses on recommendation, behavior, and anomaly, among of social media analysis. First, recommendation is vital for a variety of applications to narrow down the search space and to better guide people towards educated and personalized alternatives. In this context, the book covers supporting students, food venue, friend and paper recommendation to demonstrate the power of social media data analysis. Secondly, this book treats behavior analysis and understanding as important for a variety of applications, including inspiring behavior from discussion platforms, determining user choices, detecting following patterns, crowd behavior modeling for emergency evacuation, tracking community structure, etc. Third, fraud and anomaly detection have been well tackled based on social media analysis. This has is illustrated in this book by identifying anomalous nodes in a network, chasing undetected fraud processes, discovering hidden knowledge, detecting clickbait, etc. With this wide coverage, the book forms a good source for practitioners and researchers, including instructors and students. 410 0$aLecture Notes in Social Networks,$x2190-5428 606 $aSociophysics 606 $aEconophysics 606 $aSocial sciences?Data processing 606 $aSocial sciences?Computer programs 606 $aBig data 606 $aApplication software 606 $aData-driven Science, Modeling and Theory Building$3https://scigraph.springernature.com/ontologies/product-market-codes/P33030 606 $aComputational Social Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/X34000 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 606 $aComputer Appl. in Social and Behavioral Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/I23028 615 0$aSociophysics. 615 0$aEconophysics. 615 0$aSocial sciences?Data processing. 615 0$aSocial sciences?Computer programs. 615 0$aBig data. 615 0$aApplication software. 615 14$aData-driven Science, Modeling and Theory Building. 615 24$aComputational Social Sciences. 615 24$aBig Data/Analytics. 615 24$aComputer Appl. in Social and Behavioral Sciences. 676 $a302.30285 702 $aKaya$b Mehmet$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBirinci$b ?uayip$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKawash$b Jalal$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAlhajj$b Reda$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418439103316 996 $aPutting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation$91882365 997 $aUNISA