國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Data science for fake newssurveys an...

P, Deepak.

Data science for fake newssurveys and perspectives /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data science for fake newsby Deepak P ... [et al.].
其他題名:	surveys and perspectives /
其他作者:	P, Deepak.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xiv, 302 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Fake news.
電子資源:	https://doi.org/10.1007/978-3-030-62696-9
ISBN:	9783030626969$q(electronic bk.)

Data science for fake newssurveys and perspectives /
Data science for fake newssurveys and perspectives /[electronic resource] :by Deepak P ... [et al.]. - Cham :Springer International Publishing :2021. - xiv, 302 p. :ill., digital ;24 cm. - The information retrieval series,v.421871-7500 ;. - Information retrieval series ;v.37..

This book provides an overview of fake news detection, both through a variety of tutorial-style survey articles that capture advancements in the field from various facets and in a somewhat unique direction through expert perspectives from various disciplines. The approach is based on the idea that advancing the frontier on data science approaches for fake news is an interdisciplinary effort, and that perspectives from domain experts are crucial to shape the next generation of methods and tools. The fake news challenge cuts across a number of data science subfields such as graph analytics, mining of spatio-temporal data, information retrieval, natural language processing, computer vision and image processing, to name a few. This book will present a number of tutorial-style surveys that summarize a range of recent work in the field. In a unique feature, this book includes perspective notes from experts in disciplines such as linguistics, anthropology, medicine and politics that will help to shape the next generation of data science research in fake news. The main target groups of this book are academic and industrial researchers working in the area of data science, and with interests in devising and applying data science technologies for fake news detection. For young researchers such as PhD students, a review of data science work on fake news is provided, equipping them with enough know-how to start engaging in research within the area. For experienced researchers, the detailed descriptions of approaches will enable them to take seasoned choices in identifying promising directions for future research.

ISBN: 9783030626969$q(electronic bk.)

Standard No.: 10.1007/978-3-030-62696-9doiSubjects--Topical Terms:

806418
Fake news.

LC Class. No.: PN4784.F27

Dewey Class. No.: 070.4

Data science for fake newssurveys and perspectives /
LDR:02679nmm a2200337 a 4500 001 597596
003 DE-He213
005 20210430000226.0
006 m d
007 cr nn 008maaau
008 211019s2021 sz s 0 eng d
020 $a 9783030626969$q(electronic bk.)
020 $a 9783030626952$q(paper)
024 7 $a 10.1007/978-3-030-62696-9 $2 doi
035 $a 978-3-030-62696-9
040 $a GP $c GP
041 0 $a eng
050 4 $a PN4784.F27
072 7 $a UNH $2 bicssc
072 7 $a COM030000 $2 bisacsh
072 7 $a UNH $2 thema
072 7 $a UND $2 thema
082 0 4 $a 070.4 $2 23
090 $a PN4784.F27 $b D232 2021
245 0 0 $a Data science for fake news $h [electronic resource] : $b surveys and perspectives / $c by Deepak P ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xiv, 302 p. : $b ill., digital ; $c 24 cm.
490 1 $a The information retrieval series, $x 1871-7500 ; $v v.42
520 $a This book provides an overview of fake news detection, both through a variety of tutorial-style survey articles that capture advancements in the field from various facets and in a somewhat unique direction through expert perspectives from various disciplines. The approach is based on the idea that advancing the frontier on data science approaches for fake news is an interdisciplinary effort, and that perspectives from domain experts are crucial to shape the next generation of methods and tools. The fake news challenge cuts across a number of data science subfields such as graph analytics, mining of spatio-temporal data, information retrieval, natural language processing, computer vision and image processing, to name a few. This book will present a number of tutorial-style surveys that summarize a range of recent work in the field. In a unique feature, this book includes perspective notes from experts in disciplines such as linguistics, anthropology, medicine and politics that will help to shape the next generation of data science research in fake news. The main target groups of this book are academic and industrial researchers working in the area of data science, and with interests in devising and applying data science technologies for fake news detection. For young researchers such as PhD students, a review of data science work on fake news is provided, equipping them with enough know-how to start engaging in research within the area. For experienced researchers, the detailed descriptions of approaches will enable them to take seasoned choices in identifying promising directions for future research.
650 0 $a Fake news. $3 806418
650 0 $a Journalism $x Data processing. $3 327384
650 0 $a Data mining. $3 184440
650 0 $a Journalistic ethics. $3 301061
650 1 4 $a Information Storage and Retrieval. $3 274190
650 2 4 $a Systems and Data Security. $3 274481
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Media Studies. $2 ukslc $3 688971
700 1 $a P, Deepak. $3 727768
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Information retrieval series ; $v v.37. $3 775332
856 4 0 $u https://doi.org/10.1007/978-3-030-62696-9
950 $a Computer Science (SpringerNature-11645)