Version 1
: Received: 31 July 2017 / Approved: 2 August 2017 / Online: 2 August 2017 (12:38:17 CEST)
How to cite:
Khonji, M.; Iraqi, Y. De-Anonymizing Authors of Electronic Texts: A Survey on Electronic Text Stylometry. Preprints2017, 2017080003. https://doi.org/10.20944/preprints201708.0003.v1
Khonji, M.; Iraqi, Y. De-Anonymizing Authors of Electronic Texts: A Survey on Electronic Text Stylometry. Preprints 2017, 2017080003. https://doi.org/10.20944/preprints201708.0003.v1
Khonji, M.; Iraqi, Y. De-Anonymizing Authors of Electronic Texts: A Survey on Electronic Text Stylometry. Preprints2017, 2017080003. https://doi.org/10.20944/preprints201708.0003.v1
APA Style
Khonji, M., & Iraqi, Y. (2017). De-Anonymizing Authors of Electronic Texts: A Survey on Electronic Text Stylometry. Preprints. https://doi.org/10.20944/preprints201708.0003.v1
Chicago/Turabian Style
Khonji, M. and Youssef Iraqi. 2017 "De-Anonymizing Authors of Electronic Texts: A Survey on Electronic Text Stylometry" Preprints. https://doi.org/10.20944/preprints201708.0003.v1
Abstract
Electronic text stylometry is a collection of forensics methods that analyze the writing styles of input electronic texts in order to extract information about authors of the input electronic texts. Such extracted information could be the identity of the authors, or aspects of the authors, such as their gender, age group, ethnicity, etc. This survey paper presents the following contributions: 1) A description of all stylometry problems in probability terms, under a unified notation. To the best of our knowledge, this is the most comprehensive definition to date. 2) A survey of key methods, with a particular attention to data representation (or feature extraction) methods. 3) An evaluation of 23,760 feature extraction methods, which is the most comprehensive evaluation of feature extraction methods in the literature of stylometry to date. The importance of this evaluation is two fold. First, identifying the relative effectiveness of the features (since, currently, many are not evaluated jointly; e.g. syntactic n-grams are not evaluated against k-skip n-grams, and so forth). Second, thanks to our generalizations, we could evaluate novel grams, such as what we name compound grams. 4) The release of our associated Python feature extraction library, namely Fextractor. Essentially, the library generalizes all existing n-gram based feature extraction methods under the "at least l-frequent, dir-directed, k-skipped n-grams'', and allows grams to be diversely defined, including definitions that are based on high-level grammatical aspects, such as POS tags, as well as lower-level ones, such as distribution of function words, word shapes, etc. This makes the library, by far, the most extensive in this domain to date. 5) The construction, evaluation, and release of the first dataset for Emirati social media text. This evaluation represents the first evaluation of author identification against Emirati social media texts. Interestingly, we find that, when using our models and feature extraction library (Fextractor), authors could be identified significantly more accurately than what is reported with similarly sized datasets. The dataset also contains sub-datasets that represent other languages (Dutch, English, Greek and Spanish), and our findings are consistent across them.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
25 April 2018
The commenter has declared there is no conflict of interests.
Comment:
Parts of this preprint have been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
The commenter has declared there is no conflict of interests.