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Identification of pathogenic social ...

Alvari, Hamidreza.

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Identification of pathogenic social media accounts = from data to intelligence to prediction /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identification of pathogenic social media accounts/ by Hamidreza Alvari, Elham Shaabani, Paulo Shakarian.
Reminder of title:	from data to intelligence to prediction /
Author:	Alvari, Hamidreza.
other author:	Shaabani, Elham.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	ix, 95 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Online social networks - Security measures. -
Online resource:	https://doi.org/10.1007/978-3-030-61431-7
ISBN:	9783030614317

Identification of pathogenic social media accounts = from data to intelligence to prediction /
Alvari, Hamidreza.

Identification of pathogenic social media accountsfrom data to intelligence to prediction /[electronic resource] :by Hamidreza Alvari, Elham Shaabani, Paulo Shakarian. - Cham :Springer International Publishing :2021. - ix, 95 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

This book sheds light on the challenges facing social media in combating malicious accounts, and aims to introduce current practices to address the challenges. It further provides an in-depth investigation regarding characteristics of "Pathogenic Social Media (PSM),"by focusing on how they differ from other social bots (e.g., trolls, sybils and cyborgs) and normal users as well as how PSMs communicate to achieve their malicious goals. This book leverages sophisticated data mining and machine learning techniques for early identification of PSMs, using the relevant information produced by these bad actors. It also presents proactive intelligence with a multidisciplinary approach that combines machine learning, data mining, causality analysis and social network analysis, providing defenders with the ability to detect these actors that are more likely to form malicious campaigns and spread harmful disinformation. Over the past years, social media has played a major role in massive dissemination of misinformation online. Political events and public opinion on the Web have been allegedly manipulated by several forms of accounts including "Pathogenic Social Media (PSM)" accounts (e.g., ISIS supporters and fake news writers) PSMs are key users in spreading misinformation on social media - in viral proportions. Early identification of PSMs is thus of utmost importance for social media authorities in an effort toward stopping their propaganda. The burden falls to automatic approaches that can identify these accounts shortly after they began their harmful activities. Researchers and advanced-level students studying and working in cybersecurity, data mining, machine learning, social network analysis and sociology will find this book useful. Practitioners of proactive cyber threat intelligence and social media authorities will also find this book interesting and insightful, as it presents an important and emerging type of threat intelligence facing social media and the general public.

ISBN: 9783030614317

Standard No.: 10.1007/978-3-030-61431-7doiSubjects--Topical Terms:

1458430
Online social networks
--Security measures.

LC Class. No.: TK5105.59 / .A58 2021

Dewey Class. No.: 006.754

Identification of pathogenic social media accounts = from data to intelligence to prediction /
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520 $a This book sheds light on the challenges facing social media in combating malicious accounts, and aims to introduce current practices to address the challenges. It further provides an in-depth investigation regarding characteristics of "Pathogenic Social Media (PSM),"by focusing on how they differ from other social bots (e.g., trolls, sybils and cyborgs) and normal users as well as how PSMs communicate to achieve their malicious goals. This book leverages sophisticated data mining and machine learning techniques for early identification of PSMs, using the relevant information produced by these bad actors. It also presents proactive intelligence with a multidisciplinary approach that combines machine learning, data mining, causality analysis and social network analysis, providing defenders with the ability to detect these actors that are more likely to form malicious campaigns and spread harmful disinformation. Over the past years, social media has played a major role in massive dissemination of misinformation online. Political events and public opinion on the Web have been allegedly manipulated by several forms of accounts including "Pathogenic Social Media (PSM)" accounts (e.g., ISIS supporters and fake news writers) PSMs are key users in spreading misinformation on social media - in viral proportions. Early identification of PSMs is thus of utmost importance for social media authorities in an effort toward stopping their propaganda. The burden falls to automatic approaches that can identify these accounts shortly after they began their harmful activities. Researchers and advanced-level students studying and working in cybersecurity, data mining, machine learning, social network analysis and sociology will find this book useful. Practitioners of proactive cyber threat intelligence and social media authorities will also find this book interesting and insightful, as it presents an important and emerging type of threat intelligence facing social media and the general public.
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