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Social media analysis for event detection

Record Type:	Electronic resources : Monograph/item
Title/Author:	Social media analysis for event detectionedited by Tansel Ozyer.
other author:	Ozyer, Tansel.
Published:	Cham :Springer International Publishing :2022.
Description:	vi, 229 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Social mediaData processing.
Online resource:	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

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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.
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