

Georgi Karadzhov, Tsvetomila Mihaylova, Yasen Kiprov, Georgi Georgiev, Ivan Koychev, Preslav Nakov
Best of the Labs Track at CLEF-2017
Publication year: 2017

Best-of-labs track paper

Users posting online expect to remain anonymous unless they have logged in, which is often needed for them to be able to discuss freely on various topics. Preserving the anonymity of a text’s writer can be also important in some other contexts, e.g., in the case of witness protection or anonymity programs. However, each person has his/her own style of writing, which can be analyzed
using stylometry, and as a result, the true identity of the author of a piece of text can be revealed even if s/he has tried to hide it. Thus, it could be helpful to design automatic tools that can help a person obfuscate his/her identity when writing text. In particular, here we propose an approach that changes the text so that it is pushed towards average values for some general stylometric characteristics, thus making the use of these characteristics less discriminative. The approach consists of three main steps: first, we calculate the values for some popular stylometric metrics that can indicate authorship; then we apply various transformations to the text, so that these metrics are adjusted towards the average level, while preserving the semantics and the soundness of the text; and finally, we add random noise. This approach turned out to be very efficient and yielded the best performance on the Author Obfuscation task at the PAN-2016 competition.

SU@ PAN’2016: Author Obfuscation—Notebook for PAN at CLEF 2016

Tsvetomila Mihaylova, Georgi Karadzhov, Preslav Nakov, Yasen Kiprov, Georgi Georgiev, Ivan Koychev
In Conference and Labs of the Evaluation Forum, CLEF, 2016
Publication year: 2016

The anonymity of a text’s writer is an important topic for some domains, such as witness protection and anonymity programs. Stylometry can be used to reveal the true author of a text even if s/he wishes to hide his/her identity. In this paper, we present our approach for hiding an author’s identity by masking their style, which we developed for the Author Obfuscation task, part of the PAN-2016 competition. The approach consists of three main steps: the first one is an evaluation of different metrics in the text that can indicate authorship; the second one is the application of various transformations, so that those metrics of the target text are adjusted towards the average level, while still keeping the meaning and the soundness of the text; as a final step, we are adding random noise to the text. Our system showed the best performance for masking the author style.