Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/327968
Type: Artigo
Title: Authorship Attribution For Social Media Forensics
Authorship attribution for social media forensics
Author: Rocha, Anderson
Scheirer, Walter
Forstall, Christopher
Cavalcante, Thiago
Theophilo, Antonio
Shen, Bingyu
Carvalho, Ariadne R. B.
Stamatatos, Efstathios
Abstract: The 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.
The 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 te
Subject: Authorship Attribution
Forensics
Social Media
Machine Learning
Computational Linguistics
Stylometry
Autoria
Ciência forense digital
Mídia social
Aprendizado de máquina
Linguistica computacional
Algoritmos
Country: Estados Unidos
Editor: Institute of Electrical and Electronics Engineers
Citation: Ieee Transactions On Information Forensics And Security. Ieee-inst Electrical Electronics Engineers Inc, v. 12, p. 5 - 33, 2017.
Rights: fechado
Fechado
Identifier DOI: 10.1109/TIFS.2016.2603960
Address: https://ieeexplore.ieee.org/document/7555393
Date Issue: 2017
Appears in Collections:IC - Artigos e Outros Documentos

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