Detecting Hoaxes, Frauds, and Deception in Writing Style Online.
In digital forensics, questions often arise about the authors of documents: their identity, demographic background, and whether they can be
linked to other documents. The ﬁeld of stylometry uses linguistic features and machine learning techniques to answer these questions.
While stylometry techniques can identify authors with high accuracy in non-adversarial scenarios, their accuracy is reduced
to random guessing when faced with authors who intentionally obfuscate their writing style or attempt to imitate that of another author. While these results
are good for privacy, they raise concerns about fraud. We argue that some linguistic features change when people hide their writing style and by identifying\those features, stylistic deception can be recognized. The major contribution of this work is a method for detecting stylistic deception in written documents. We
show that using a large feature set, it is possible to distinguish regular documents from deceptive documents with 96.6% accuracy (F-measure). We also
present an analysis of linguistic features that can be modiﬁed to hide writing style.
- "Use Fewer Instances of the Letter "i": Toward Writing Style Anonymization." Andrew McDonald, Sadia Afroz, Aylin Caliskan, Ariel Stolerman and Rachel Greenstadt. The 12th Privacy Enhancing Technologies Symposium.
- "Detecting Hoaxes, Frauds, and Deception in Writing Style Online." Afroz/Brennan/Greenstadt. IEEE Symposium on Security and Privacy '12.
- "Practical Attacks Against Authorship Recognition Techniques." Brennan/Greenstadt. IAAI '09.
- "Deceiving Authorship Detection." The 28th Chaos Communication Congress (CCC), Berlin.
Sadia Afroz is a PhD candidate at Drexel University. She works in machine learning and security with Rachel Greenstadt. Website: https://www.cs.drexel.edu/~sa499/