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dc.contributor.CRUESPUNIVERSIDADE DE ESTADUAL DE CAMPINASpt_BR;;;;;;; stamatatos@aegean.grpt_BR
dc.titleAuthorship Attribution For Social Media Forensicsen
dc.contributor.authorAnderson; Scheirerpt_BR
dc.contributor.authorWalter J.; Forstallpt_BR
dc.contributor.authorChristopher W.; Cavalcantept_BR
dc.contributor.authorThiago; Theophilopt_BR
dc.contributor.authorAntonio; Shenpt_BR
dc.contributor.authorBingyu; Carvalhopt_BR
dc.contributor.authorAriadne R. B.; Stamatatospt_BR
unicamp.authorCarvalho, Ariadne R. B.] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP, Brazilpt_BR[Theophilo, Antonio] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP, Brazilpt_BR[Rocha, Andersonpt_BR, Thiagopt_BR[Scheirer, Walter J.pt_BR, Christopher W.pt_BR, Bingyu] Univ Notre Dame, Dept Comp Sci & Engn, Notre Dame, IN 46556 USApt_BR[Theophilo, Antonio] Ctr Informat Technol Renato Archer, BR-13069901 Campinas, SP, Brazilpt_BR[Stamatatos, Efstathios] Univ Aegean, Dept Informat & Commun Syst Engn, Karlovassi 83200, Greecept_BR
dc.subjectAuthorship Attributionen
dc.subjectSocial Mediaen
dc.subjectMachine Learningen
dc.subjectComputational Linguisticsen
dc.description.abstractThe veil of anonymity provided by smartphones with pre-paid SIM cards, public Wi-Fi hotspots, and distributed networks like Tor has drastically complicated the task of identifying users of social media during forensic investigations. In some cases, the text of a single posted message will be the only clue to an author's identity. How can we accurately predict who that author might be when the message may never exceed 140 characters on a service like Twitter? For the past 50 years, linguists, computer scientists, and scholars of the humanities have been jointly developing automated methods to identify authors based on the style of their writing. All authors possess peculiarities of habit that influence the form and content of their written works. These characteristics can often be quantified and measured using machine learning algorithms. In this paper, we provide a comprehensive review of the methods of authorship attribution that can be applied to the problem of social media forensics. Furthermore, we examine emerging supervised learning-based methods that are effective for small sample sizes, and provide step-by-step explanations for several scalable approaches as instructional case studies for newcomers to the field. We argue that there is a significant need in forensics for new authorship attribution algorithms that can exploit context, can process multi-modal data, and are tolerant to incomplete knowledge of the space of all possible authors at training time.en
dc.relation.ispartofIEEE Transactions on Information Forensics and Securitypt_BR
dc.publisherIEEE-Inst Electrical Electronics Engineers Incpt_BR
dc.identifier.citationIeee Transactions On Information Forensics And Security. Ieee-inst Electrical Electronics Engineers Inc, v. 12, p. 5 - 33, 2017.pt_BR
dc.description.sponsorshipBrazilian Coordination for the Improvement of Higher Education and Personnel - CAPESpt_BR
dc.description.sponsorshipSao Paulo Research Foundation - FAPESP [15/19222-9]pt_BR
dc.description.sponsorship1Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)pt_BR
dc.description.sponsorship1Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.description.provenanceMade available in DSpace on 2017-11-13T13:22:45Z (GMT). No. of bitstreams: 1 000388122000001.pdf: 7016862 bytes, checksum: 24f5a657f5e3814effdb852f0f2deee2 (MD5) Previous issue date: 2017en
Appears in Collections:Unicamp - Artigos e Outros Documentos

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