BN-AuthProf: Benchmarking Machine Learning for Bangla Author Profiling on Social Media Texts

TL;DR

This paper introduces BN-AuthProf, a new dataset for Bangla social media texts, benchmarks machine learning methods for author profiling, and demonstrates promising results in gender and age classification.

Contribution

It creates and evaluates the first comprehensive Bangla author profiling dataset and benchmarks multiple machine learning techniques for demographic prediction.

Findings

Support Vector Machine achieved 80% accuracy in gender classification.

Multinomial Naive Bayes achieved 91% accuracy in age classification.

The study demonstrates machine learning's effectiveness for Bangla author profiling.

Abstract

Author profiling, the analysis of texts to uncover attributes such as gender and age of the author, has become essential with the widespread use of social media platforms. This paper focuses on author profiling in the Bangla language, aiming to extract valuable insights about anonymous authors based on their writing style on social media. The primary objective is to introduce and benchmark the performance of machine learning approaches on a newly created Bangla Author Profiling dataset, BN-AuthProf. The dataset comprises 30,131 social media posts from 300 authors, labeled by their age and gender. Authors' identities and sensitive information were anonymized to ensure privacy. Various classical machine learning and deep learning techniques were employed to evaluate the dataset. For gender classification, the best accuracy achieved was 80% using Support Vector Machine (SVM), while a Multinomial Naive Bayes (MNB) classifier achieved the best F1 score of 0.756. For age classification, MNB attained a maximum accuracy score of 91% with an F1 score of 0.905. This research highlights the effectiveness of machine learning in gender and age classification for Bangla author profiling, with practical implications spanning marketing, security, forensic linguistics, education, and criminal investigations, considering privacy and biases.