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.
