# A Comparative Analysis of Distributional Term Representations for Author Profiling in Social Media

## TL;DR

This paper evaluates various distributional term representations for author profiling in social media, demonstrating their effectiveness and interpretability compared to traditional topic-based methods.

## Contribution

It introduces a new framework for supervised author profiling using distributional term representations and provides a comprehensive comparison with classic approaches.

## Key findings

- DTRs achieve competitive accuracy in author profiling.
- DTRs offer meaningful interpretability.
- Distributional term representations outperform some traditional methods.

## Abstract

Author Profiling (AP) aims at predicting specific characteristics from a group of authors by analyzing their written documents. Many research has been focused on determining suitable features for modeling writing patterns from authors. Reported results indicate that content-based features continue to be the most relevant and discriminant features for solving this task. Thus, in this paper, we present a thorough analysis regarding the appropriateness of different distributional term representations (DTR) for the AP task. In this regard, we introduce a novel framework for supervised AP using these representations and, supported on it. We approach a comparative analysis of representations such as DOR, TCOR, SSR, and word2vec in the AP problem. We also compare the performance of the DTRs against classic approaches including popular topic-based methods. The obtained results indicate that DTRs are suitable for solving the AP task in social media domains as they achieve competitive results while providing meaningful interpretability.