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

• URL: http://arxiv.org/abs/2310.00347v3
• Date: Wed, 17 Apr 2024 11:48:11 GMT
• Title: Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis
• Authors: Shaina Raza, Oluwanifemi Bamgbose, Veronica Chatrath, Shardul Ghuge, Yan Sidyakin, Abdullah Y Muaad,
• Abstract summary: We present the Contextualized Bi-Directional Dual Transformer (CBDT) textcolorgreenfaLeaf classifier. We have prepared a dataset specifically for training these models to identify and locate biases in texts. Our evaluations across various datasets demonstrate CBDT textcolorgreen effectiveness in distinguishing biased narratives from neutral ones and identifying specific biased terms.
• Score: 1.8692054990918079
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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