東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Putting social media and networking ...

Kaya, Mehmet.

FindBook

Google Book

Amazon

博客來

Putting social media and networking data in practice for education, planning, prediction and recommendation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Putting social media and networking data in practice for education, planning, prediction and recommendation/ edited by Mehmet Kaya ... [et al.].
其他作者:	Kaya, Mehmet.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xiii, 237 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Online social networks. -
電子資源:	https://doi.org/10.1007/978-3-030-33698-1
ISBN:	9783030336981

Putting social media and networking data in practice for education, planning, prediction and recommendation
Putting social media and networking data in practice for education, planning, prediction and recommendation[electronic resource] /edited by Mehmet Kaya ... [et al.]. - Cham :Springer International Publishing :2020. - xiii, 237 p. :ill., digital ;24 cm. - Lecture notes in social networks,2190-5428. - Lecture notes in social networks..

This 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.

ISBN: 9783030336981

Standard No.: 10.1007/978-3-030-33698-1doiSubjects--Topical Terms:

624374
Online social networks.

LC Class. No.: HM742 / .P888 2020

Dewey Class. No.: 006.754

Putting social media and networking data in practice for education, planning, prediction and recommendation
LDR:02216nmm a2200337 a 4500 001 2215296
003 DE-He213
005 20200528142044.0
006 m d
007 cr nn 008maaau
008 201119s2020 sz s 0 eng d
020 $a 9783030336981 $q (electronic bk.)
020 $a 9783030336974 $q (paper)
024 7 $a 10.1007/978-3-030-33698-1 $2 doi
035 $a 978-3-030-33698-1
040 $a GP $c GP
041 0 $a eng
050 4 $a HM742 $b .P888 2020
072 7 $a JHBC $2 bicssc
072 7 $a SCI064000 $2 bisacsh
072 7 $a JHBC $2 thema
072 7 $a PSAF $2 thema
082 0 4 $a 006.754 $2 23
090 $a HM742 $b .P993 2020
245 0 0 $a Putting social media and networking data in practice for education, planning, prediction and recommendation $h [electronic resource] / $c edited by Mehmet Kaya ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xiii, 237 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in social networks, $x 2190-5428
520 $a This 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.
650 0 $a Online social networks. $3 624374
650 1 4 $a Data-driven Science, Modeling and Theory Building. $3 2210495
650 2 4 $a Computational Social Sciences. $3 3220598
650 2 4 $a Big Data/Analytics. $3 2186785
650 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 892702
700 1 $a Kaya, Mehmet. $3 3226537
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Lecture notes in social networks. $3 2058983
856 4 0 $u https://doi.org/10.1007/978-3-030-33698-1
950 $a Physics and Astronomy (Springer-11651)