Distance measures in author profiling

Determining some demographics about the author of a document (e.g., gender, age) has attracted many studies during the last decade. To solve this author profiling task, various classification models have been proposed based on stylistic features (e.g., function word frequencies, n-gram of letters or words, POS distributions), as well as various vocabulary richness or overall stylistic measures. To determine the targeted category, different distance measures have been suggested without one approach clearly dominating all others. In this paper, 24 distance measures are studied, extracted from five general families of functions. Moreover, six theoretical properties are presented and we show that the Tanimoto or Matusita distance measures respect all proposed properties. To complement this analysis, 13 test collections extracted from the last CLEF evaluation campaigns are employed to evaluate empirically the effectiveness of these distance measures. This test set covers four languages (English, Spanish, Dutch, and Italian), four text genres (blogs, tweets, reviews, and social media) with respect to two genders and between four to five age groups. The empirical evaluations indicate that the Canberra or Clark distance measures tend to produce better effectiveness than the rest, at least in the context of an author profiling task. Moreover, our experiments indicate that having a training set closely related to the test set (e.g., the same collection) has a clear impact on the overall performance. The gender accuracy rate is decreased by 7% (19% for the age) when using the same text genre during the training compared to using the same collection (leaving-one-out methodology). Employing a different text genre in the training and in the test phases tends to hurt the overall performance, showing a decrease of the final accuracy rate of around 11% for the gender classification to 26% for the age.