語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
到查詢結果
[ author_sort:"ozyer, tansel." ]
切換:
標籤
|
MARC模式
|
ISBD
Social media analysis for event detection
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Social media analysis for event detectionedited by Tansel Ozyer.
其他作者:
Ozyer, Tansel.
出版者:
Cham :Springer International Publishing :2022.
面頁冊數:
vi, 229 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
標題:
Social mediaData processing.
電子資源:
https://doi.org/10.1007/978-3-031-08242-9
ISBN:
9783031082429$q(electronic bk.)
Social media analysis for event detection
Social media analysis for event detection
[electronic resource] /edited by Tansel Ozyer. - Cham :Springer International Publishing :2022. - vi, 229 p. :ill., digital ;24 cm. - Lecture notes in social networks,2190-5436. - Lecture notes in social networks..
Chapter 1. A network-based approach to understanding international cooperation in environmental protection (Andreea Nita) -- Chapter 2. Critical Mass and Data Integrity Diagnostics of a Twitter Social Contagion Monitor (Amruta Deshpande) -- Chapter 3. TenFor: Tool to Mine Interesting Events from Security Forums Leveraging Tensor Decomposition (Risul Islam) -- Chapter 4. Profile Fusion in Social Networks: A Data-Driven Approach -Youcef Benkhedda) -- Chapter 5. RISECURE: Metro Transit Disruptions Detection Using Social Media Mining And Graph Convolution (Omer Zulqar) -- Chapter 6. Local Taxonomy Construction: An Information Retrieval Approach Using Representation Learning (Mayank Kejriwal) -- Chapter 7. The evolution of online sentiments across Italy during first and second wave of the COVID-19 pandemic (Francesco Scotti) -- Chapter 8. Inferring Degree of Localization and Popularity of Twitter Topics and Persons using Temporal Features (Aleksey Panasyuk) -- Chapter 9. Covid-19 and Vaccine Tweet Analysis (Eren Alp)
This book includes chapters which discuss effective and efficient approaches in dealing with various aspects of social media analysis by using machine learning techniques from clustering to deep learning. A variety of theoretical aspects, application domains and case studies are covered to highlight how it is affordable to maximize the benefit of various applications from postings on social media platforms. Social media platforms have significantly influenced and reshaped various social aspects. They have set new means of communication and interaction between people, turning the whole world into a small village where people with internet connect can easily communicate without feeling any barriers. This has attracted the attention of researchers who have developed techniques and tools capable of studying various aspects of posts on social media platforms with main concentration on Twitter. This book addresses challenging applications in this dynamic domain where it is not possible to continue applying conventional techniques in studying social media postings. The content of this book helps the reader in developing own perspective about how to benefit from machine learning techniques in dealing with social media postings and how social media postings may directly influence various applications.
ISBN: 9783031082429$q(electronic bk.)
Standard No.: 10.1007/978-3-031-08242-9doiSubjects--Topical Terms:
902663
Social media
--Data processing.
LC Class. No.: HM742 / .S63 2022
Dewey Class. No.: 302.230285
Social media analysis for event detection
LDR
:03363nmm a2200337 a 4500
001
631154
003
DE-He213
005
20221018222830.0
006
m d
007
cr nn 008maaau
008
230411s2022 sz s 0 eng d
020
$a
9783031082429$q(electronic bk.)
020
$a
9783031082412$q(paper)
024
7
$a
10.1007/978-3-031-08242-9
$2
doi
035
$a
978-3-031-08242-9
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
HM742
$b
.S63 2022
072
7
$a
UN
$2
bicssc
072
7
$a
COM031000
$2
bisacsh
072
7
$a
UN
$2
thema
082
0 4
$a
302.230285
$2
23
090
$a
HM742
$b
.S678 2022
245
0 0
$a
Social media analysis for event detection
$h
[electronic resource] /
$c
edited by Tansel Ozyer.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2022.
300
$a
vi, 229 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Lecture notes in social networks,
$x
2190-5436
505
0
$a
Chapter 1. A network-based approach to understanding international cooperation in environmental protection (Andreea Nita) -- Chapter 2. Critical Mass and Data Integrity Diagnostics of a Twitter Social Contagion Monitor (Amruta Deshpande) -- Chapter 3. TenFor: Tool to Mine Interesting Events from Security Forums Leveraging Tensor Decomposition (Risul Islam) -- Chapter 4. Profile Fusion in Social Networks: A Data-Driven Approach -Youcef Benkhedda) -- Chapter 5. RISECURE: Metro Transit Disruptions Detection Using Social Media Mining And Graph Convolution (Omer Zulqar) -- Chapter 6. Local Taxonomy Construction: An Information Retrieval Approach Using Representation Learning (Mayank Kejriwal) -- Chapter 7. The evolution of online sentiments across Italy during first and second wave of the COVID-19 pandemic (Francesco Scotti) -- Chapter 8. Inferring Degree of Localization and Popularity of Twitter Topics and Persons using Temporal Features (Aleksey Panasyuk) -- Chapter 9. Covid-19 and Vaccine Tweet Analysis (Eren Alp)
520
$a
This book includes chapters which discuss effective and efficient approaches in dealing with various aspects of social media analysis by using machine learning techniques from clustering to deep learning. A variety of theoretical aspects, application domains and case studies are covered to highlight how it is affordable to maximize the benefit of various applications from postings on social media platforms. Social media platforms have significantly influenced and reshaped various social aspects. They have set new means of communication and interaction between people, turning the whole world into a small village where people with internet connect can easily communicate without feeling any barriers. This has attracted the attention of researchers who have developed techniques and tools capable of studying various aspects of posts on social media platforms with main concentration on Twitter. This book addresses challenging applications in this dynamic domain where it is not possible to continue applying conventional techniques in studying social media postings. The content of this book helps the reader in developing own perspective about how to benefit from machine learning techniques in dealing with social media postings and how social media postings may directly influence various applications.
650
0
$a
Social media
$x
Data processing.
$3
902663
650
0
$a
Deep learning (Machine learning)
$3
913129
650
0
$a
Artificial intelligence.
$3
194058
650
1 4
$a
Data Science.
$3
913495
650
2 4
$a
Social Media.
$3
742054
650
2 4
$a
Natural Language Processing (NLP)
$3
826373
650
2 4
$a
Graph Theory.
$3
522732
650
2 4
$a
Machine Learning.
$3
833608
700
1
$a
Ozyer, Tansel.
$3
558016
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer Nature eBook
830
0
$a
Lecture notes in social networks.
$3
679300
856
4 0
$u
https://doi.org/10.1007/978-3-031-08242-9
950
$a
Computer Science (SpringerNature-11645)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000220810
電子館藏
1圖書
電子書
EB HM742 .S678 2022 2022
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
https://doi.org/10.1007/978-3-031-08242-9
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入