# A White-Box Sociolinguistic Model for Gender Detection
## 5 Conclusions  
Is it possible to identify the author's gender from a given text? This is the question we investigated in this paper. The rise of social media, the fact that text is the most prevalent media type used in our digital activity, and people's tendency to hide their identity on social media platforms have shown the potential of authorship profiling. In this paper, we focused on gender identification, a subtask of the authorship profiling problem that aims at determining the gender of the author of a given text, and this has revealed an interesting research area that could benefit forensics, marketing analysis, advertising, sociolinguistics, etc. Classifying the gender of a person based on short messages is a difficult task since we have to deal with short length and multi-content text. The main goal of our research is to determine sociolinguistic patterns related to gender that could improve automatic gender detection in author profiling tasks. In fact, to claim that gender identification is possible means to assume that men and women generally use different classes of language and that we can identify linguistic features that indicate gender. However, identifying a set of features that indicate gender is still an open research problem. In this paper, Machine-Learning techniques were used to analyze a Spanish social media corpus in order to obtain linguistic patterns to design a computational model for gender detection. A tree-based computational model made up of 198 features was proposed. Unlike previous work, we handled a reduced set of features that covered a wide linguistic spectrum (orthographic, morphological, lexical, syntactic, pragmatic-discursive, and digital). A decision tree classifier to evaluate the performance of feature combinations was implemented. Experiments on our corpus indicated an accuracy up to 59.2% in identifying gender. Our experiments also indicated that digital, lexical, and syntactic features were significant gender discriminators. Despite that our model reached less accuracy than other models, we obtained competitive results handling less than 1% of the number of features used in more accurate models. In modeling our problem, we made a decision regarding the trade-off between model accuracy and model interpretation. In our work, the interpretability of the model was prioritized over the classification accuracy. Although opaque methods in gender detection generally obtain higher accuracy, we chose to pay a penalty in terms of predictive performance when selecting an interpretable model that allows for human/linguistic understanding. To find linguistic patterns correlated to gender is a common interest in gender identification tasks (within the area of author profiling) and in the field of sociolinguistics. Therefore, the close collaboration between researchers in these two areas can produce better results that benefit both research fields. This is why we claim that the interdisciplinarity is a must when dealing with gender, language, and computation. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Feature** & **Importance** & **Female** & **Male** \\ \hline Exclamative sentences & 0.266 & 1.201 & 0.850 \\ Existential presuppositions proper names & 0.259 & 4.438 & 6.227 \\ Personal deixis & 0.161 & 13.273 & 10.841 \\ Spatial deixis & 0.123 & 3.225 & 3.390 \\ Ex. pr. det. phrases with defined interpretation & 0.058 & 10.428 & 12.064 \\ Temporal deixis & 0.028 & 2.181 & 1.812 \\ Tabulations & 0.027 & 3.823 & 3.152 \\ Line breaks & 0.024 & 18.515 & 16.480 \\ Gratitude expressions & 0.022 & 0.174 & 0.142 \\ Existential presuppositions & 0.018 & 17.642 & 20.967 \\ Lexical presuppositions & 0.014 & 1.958 & 1.744 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Pragmatics-discursive feature importance and mean values. As future research avenues, we will replicate this computational analysis on other PAN-AP datasets in order to validate some sociolinguistic patterns.