Loading [MathJax]/extensions/TeX/AMSmath.js
Authorship Attribution vs. Adversarial Authorship from a LIWC and Sentiment Analysis Perspective | IEEE Conference Publication | IEEE Xplore

Authorship Attribution vs. Adversarial Authorship from a LIWC and Sentiment Analysis Perspective


Abstract:

Although Stylometry has been effectively used for Authorship Attribution, there is a growing number of methods being developed that allow authors to mask their identity [...Show More

Abstract:

Although Stylometry has been effectively used for Authorship Attribution, there is a growing number of methods being developed that allow authors to mask their identity [2, 13]. In this paper, we investigate the usage of non-traditional feature sets for Authorship Attribution. By using non-traditional feature sets, one may be able to reveal the identity of adversarial authors who are attempting to evade detection from Authorship Attribution systems that are based on more traditional feature sets. In addition, we demonstrate how GEFeS (Genetic & Evolutionary Feature Selection) can be used to evolve high-performance hybrid feature sets composed of two non-traditional feature sets for Authorship Attribution: LIWC (Linguistic Inquiry & Word Count) and Sentiment Analysis. These hybrids were able to reduce the Adversarial Effectiveness on a test set presented in [2] by approximately 33.4%.
Date of Conference: 18-21 November 2018
Date Added to IEEE Xplore: 31 January 2019
ISBN Information:
Conference Location: Bangalore, India

References

References is not available for this document.