{'fraction_non_alphanumeric': 0.021339220014716703, 'fraction_numerical': 0.004415011037527594, 'mean_word_length': 5.325581395348837, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Author Gender Prediction in Russian Social Media Texts

Presently natural language processing for social media, in particular in the field of sentiment analysis and topic modeling, is gaining momentum for Russian texts. However, Slavic languages including Russian are still insufficiently explored in terms of computational sociolinguistics and authorship profiling (i.e. automatic identification of latent demographic features of online users such as gender, age, personality based on their texts). Being able to predict these features with a high degree of accuracy would certainly benefit marketing, psychological studies and security. In this paper we are attempting to build classifiers to predict gender of the author in Russian Twitter and Facebook texts and explore the effect of the cross-genre evaluation. We used the most common lemmas, a set of morphological and syntactic parameters as well as the part-ofspeech (POS) trigrams as features and multiple classifiers to train and test models. Twitter corpus was used for training, Facebook and test set of Twitter corpus were used for testing. The best models for Twitter were ExtraTreesClassifier and RandomForestClassifier with accuracy 0.72 and linearSVM for Facebook (0.71). The obtained results are comparable with stateof-the art results for Russian language for the texts of different genres.