An Attention-Based Denoising Framework for Personality Detection in Social Media Texts

Abstract

In social media networks, users produce a large amount of text content anytime, providing researchers with an invaluable approach to digging for personality-related information. Personality detection based on user-generated text is a method with broad application prospects, such as for constructing user portraits. The presence of significant noise in social media texts hinders personality detection. However, previous studies have not delved deeper into addressing this challenge. Inspired by the scanning reading technique, we propose an attention-based information extraction mechanism (AIEM) for long texts, which is applied to quickly locate valuable pieces of text, and fully integrate beneficial semantic information. Then, we provide a novel attention-based denoising framework (ADF) for personality detection tasks and achieve state-of-the-art performance on two commonly used datasets. Notably, we obtain an average accuracy improvement of 10.2% on the gold standard Twitter-Myers-Briggs Type Indicator (Twitter-MBTI) dataset. We made our code publicly available on GitHub\footnote{https://github.com/Once2gain/PersonalityDetection}. We shed light on how AIEM works to magnify personality-related signals through a case study.