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001433355 001__ 1433355
001433355 003__ OCoLC
001433355 005__ 20230309003601.0
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001433355 019__ $$a1229917439$$a1236306531$$a1238201120$$a1241065895$$a1249943813
001433355 020__ $$a9783030614317$$q(electronic bk.)
001433355 020__ $$a303061431X$$q(electronic bk.)
001433355 020__ $$z3030614301
001433355 020__ $$z9783030614300
001433355 0247_ $$a10.1007/978-3-030-61431-7$$2doi
001433355 035__ $$aSP(OCoLC)1229126370
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001433355 049__ $$aISEA
001433355 050_4 $$aTK5105.59
001433355 08204 $$a006.7/54$$223
001433355 1001_ $$aAlvari, Hamidreza.
001433355 24510 $$aIdentification of pathogenic social media accounts :$$bfrom data to intelligence to prediction /$$cHamidreza Alvari, Elham Shaabani, Paulo Shakarian.
001433355 260__ $$aCham :$$bSpringer,$$c2021.
001433355 300__ $$a1 online resource
001433355 336__ $$atext$$btxt$$2rdacontent
001433355 337__ $$acomputer$$bc$$2rdamedia
001433355 338__ $$aonline resource$$bcr$$2rdacarrier
001433355 347__ $$atext file
001433355 347__ $$bPDF
001433355 4901_ $$aSpringerBriefs in Computer Science
001433355 504__ $$aIncludes bibliographical references.
001433355 5050_ $$aIntroduction -- Characterizing pathogenic social media accounts -- Unsupervised pathogenic social media accounts detection without content or network structure -- Early detection of pathogenic social media accounts -- Semi-supervised causal inference for identifying pathogenic social media accounts -- Graph-based semi-supervised and supervised approaches for detecting pathogenic social media acounts -- Feature-driven method for identifying pathogenic social media accounts -- Conclusion.
001433355 506__ $$aAccess limited to authorized users.
001433355 520__ $$aThis 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.
001433355 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 8, 2021).
001433355 650_0 $$aOnline social networks$$xSecurity measures.
001433355 650_0 $$aComputer networks$$xSecurity measures.
001433355 650_6 $$aRéseaux sociaux (Internet)$$xSécurité$$xMesures.
001433355 650_6 $$aRéseaux d'ordinateurs$$xSécurité$$xMesures.
001433355 655_0 $$aElectronic books.
001433355 7001_ $$aShaabani, Elham.
001433355 7001_ $$aShakarian, Paulo.
001433355 77608 $$iPrint version:$$z3030614301$$z9783030614300$$w(OCoLC)1195459019
001433355 830_0 $$aSpringerBriefs in computer science.
001433355 852__ $$bebk
001433355 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-61431-7$$zOnline Access$$91397441.1
001433355 909CO $$ooai:library.usi.edu:1433355$$pGLOBAL_SET
001433355 980__ $$aBIB
001433355 980__ $$aEBOOK
001433355 982__ $$aEbook
001433355 983__ $$aOnline
001433355 994__ $$a92$$bISE