Estimating the Similarities Between Texts of Right-Handed and Left-Handed Males and Females

  • Tatiana Litvinova
  • Pavel Seredin
  • Olga Litvinova
  • Ekaterina Ryzhkova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10456)

Abstract

Identifying the characteristics of text authors is of critical importance for marketing, security, etc., and there has been a growing interest in this issue recently. A major feature to be researched using text analysis has been gender. Despite a lot of studies that have obviously contributed to the progress in the field, identification of gender with text authors so far remains challenging and daunting. One of the reasons is that current research shows no consideration of the mutual influences of various individual characteristics including gender and laterality. In this paper, using the material of a specially designed corpus of Russian texts named RusNeuroPsych, including the neuropsychological data of the authors, we calculated the distance between texts written by right-handed and left-handed males and females (4 classes). For this study we have chosen handedness as one of the most important laterality measures. In order to calculate the distance between the classes, a formula measuring the Wave-Hedges distance was employed. The text parameters were topic-independent and frequent (the indices of lexical diversity, a variety of parts of speech ratios, etc.). It was shown that texts by authors of different genders but with an identical type of handedness are more similar linguistically than those by individuals of the same gender but with a different type of manual preference. We suppose that it could be useful to build a classifier for classes “gender + handedness” instead of predicting gender itself.

Keywords

Gender attribution Authorship profiling Handedness Laterality Distance measures Russian language 

Notes

Acknowledgments

This research is financially supported by the Russian Science Foundation, project No. 16-18-10050, “Identifying the Gender and Age of Online Chatters Using Formal Parameters of their Texts”.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.RusProfiling LabVoronezhRussia
  2. 2.Voronezh State UniversityVoronezhRussia
  3. 3.The Kurchatov InstituteMoscowRussia

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