A visual approach for age and gender identification on Twitter

TL;DR

This paper explores the use of visual data from Twitter images to improve age and gender identification, demonstrating the potential of visual modality in author profiling beyond traditional text analysis.

Contribution

It introduces a novel approach by incorporating visual information from Twitter images for age and gender prediction, extending existing datasets and evaluating its effectiveness.

Findings

Visual data enhances age and gender identification accuracy.

Visual modality provides valuable insights beyond text-based analysis.

Inclusion of posted images improves author profiling performance.

Abstract

The goal of Author Profiling (AP) is to identify demographic aspects (e.g., age, gender) from a given set of authors by analyzing their written texts. Recently, the AP task has gained interest in many problems related to computer forensics, psychology, marketing, but specially in those related with social media exploitation. As known, social media data is shared through a wide range of modalities (e.g., text, images and audio), representing valuable information to be exploited for extracting valuable insights from users. Nevertheless, most of the current work in AP using social media data has been devoted to analyze textual information only, and there are very few works that have started exploring the gender identification using visual information. Contrastingly, this paper focuses in exploiting the visual modality to perform both age and gender identification in social media, specifically in Twitter. Our goal is to evaluate the pertinence of using visual information in solving the AP task. Accordingly, we have extended the Twitter corpus from PAN 2014, incorporating posted images from all the users, making a distinction between tweeted and retweeted images. Performed experiments provide interesting evidence on the usefulness of visual information in comparison with traditional textual representations for the AP task.