GENDER-BASED ANALYSIS OF SLOVENE USER-GENERATED CONTENT

Language as a social phenomenon is subject to variation and change. Among the social factors of variation, the gender of speakers has been studied by sociolinguists with regard to the linguistic practices displayed by women and men. Advances in natural language processing have enabled researchers to model linguistic variation in large text corpora using automated approaches. In this Master Thesis we compare the language of women and men in Slovene usergenerated content. User-generated content is a fairly new but increasingly popular domain and presents an important language resource due to its size, production in real time, and the informal nature of communication. In our analysis of gender-related variation, we rely on the approaches of computational stylometry, which aims to generalize the writing style into measurable patterns that can be compared between individual authors or groups of authors. We base the experiments on Slovene blog entries and Twitter messages (tweets), which were manually annotated for the authors’ gender. In the first step, we investigate the documents with machine learning techniques. Using a clustering algorithm, we construct topic ontologies of blogs to identify the predominant topics in entries by female and male bloggers. We then present automated methods that explore whether gender-related linguistic variation can provide predictive information to differentiate between women and men automatically. For this, we build two types of gender prediction models: a rule-based and a statistical classifier. The rule-based classification model takes into account the use of referential gender in verb phrases. It provides a baseline accuracy for more complex and time-consuming statistical models, for which we experiment with different features and learning algorithms. The best performing statistical models on tweets and blogs are further analyzed for the features ranked as most informative for female and male authors. Moreover, we present cross-genre experiments where the gender classifier was trained on one of the text genres and tested on the other. In the second step, we analyze the linguistic choices of female and male authors that have less to do with topic and more to do with the writing style. Specifically, we contrast the use of words typical of a writing style (e.g., profane language, emoticons, hedges and intensifiers) by applying the Mann-Whitney test for testing the statistical significance of the differences, and the squared Pearson correlation coefficient as a measure of effect size. The results of our experiments with gender prediction models show that rule-based classifiers work well, but noisy text might worsen their performance. For statistical models, the combination of Support Vector Machine as the learning algorithm and word unigrams as features achieves the best results on both tweets and blogs. The statistical analysis of stylistic variation further shows that the difference between female and male authors in the use of some discourses is statistically significant, though the effect size of gender on this variation is small. The results of cross-genre experiments with statistical models suggest that our tweet-trained model could be used for predicting gender in other Slovene user-generated content.