Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis

TL;DR

This paper introduces the CBDT transformer-based model for improved bias detection in text, combining two transformer networks to better identify and locate biases across diverse datasets and contexts.

Contribution

The paper presents a novel dual transformer model, CBDT, and a dedicated bias detection dataset, advancing the accuracy and applicability of bias identification in text analysis.

Findings

CBDT outperforms existing models in bias detection accuracy

The model effectively identifies biased terms and narratives

The dataset supports diverse linguistic and cultural bias analysis

Abstract

Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making. Traditional language models frequently face challenges in generalizing beyond their training data and are typically designed for a single task, often focusing on bias detection at the sentence level. To address this, we present the Contextualized Bi-Directional Dual Transformer (CBDT) \textcolor{green}{\faLeaf} classifier. This model combines two complementary transformer networks: the Context Transformer and the Entity Transformer, with a focus on improving bias detection capabilities. We have prepared a dataset specifically for training these models to identify and locate biases in texts. Our evaluations across various datasets demonstrate CBDT \textcolor{green} effectiveness in distinguishing biased narratives from neutral ones and identifying specific biased terms. This work paves the way for applying the CBDT \textcolor{green} model in various linguistic and cultural contexts, enhancing its utility in bias detection efforts. We also make the annotated dataset available for research purposes.