T-IFS: Authorship Attribution for Social Media Forensics

We are glad to announce that our paper entitled “Authorship Attribution for Social Media Forensics” has been accepted at the IEEE Transactions on Information Forensics and Security (T-IFS), Volume PP Issue 99, 2016.

The goal of the presented research work is to identify authors of texts through features derived from the style of their writing with social media channels. The presented technique explores peculiarities of habit that influence the form and content of how people write in popular services like Twitter.

In addition, the authors also present a detailed walk-through of supervised learning methods for authorship attribution for social media forensics and a discussion of open problems in forensic authorship attribution.

Dataset and source-code.

tifsauthorship

Computational pipeline, in which features are extracted from very small samples of text, and scalable supervised learning is deployed to train author-specific models and make predictions about unknown samples

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