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Authorship Attribution for Social Media Forensics
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.
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Explainable Authorship Identification in Cultural Heritage Applications
Setzu, M., Corbara, S., Monreale, A., Moreo, A., & Sebastiani, F. (2024) · Journal on Computing and Cultural Heritage · doi:10.1145/3654675 · Cited by 5
Metadata
- Authors
- Rocha, A., Scheirer, W. J., Forstall, C. W., Cavalcante, T., Theophilo, A., Shen, B., Carvalho, A. R. B., & Stamatatos, E.
- Affiliations
- Universidade Estadual de Campinas (UNICAMP) (BR)
- University of Notre Dame (US)
- University of the Aegean (GR)
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2017-01
- Volume
- 12
- Issue
- 1
- Pages
- 5-33
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2508310662
- Cited by
- 222
- References
- 207
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 13 · 2023: 25 · 2022: 22 · 2021: 39 · 2020: 43 · 2019: 28 · 2018: 27 · 2017: 14
- FWCI
- 18.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Funders
- Brazilian Coordination for the Improvement of Higher Education and Personnel — CAPES (DeepEyes project)
- São Paulo Research Foundation – FAPESP (through the DéjàVu Project) (15/19222-9)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- authorship attribution, social media forensics, stylistic analysis, user identification, anonymity, multimodal data, forensic text analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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