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

Bias detection in text is imperative due to its role in reinforcing negative stereotypes, disseminating misinformation, and influencing decisions. Current language models often fall short in generalizing beyond their training sets. In response, we introduce the Contextualized Bi-Directional Dual Transformer (CBDT) Classifier. This novel architecture utilizes two synergistic transformer networks: the Context Transformer and the Entity Transformer, aiming for enhanced bias detection. Our dataset preparation follows the FAIR principles, ensuring ethical data usage. Through rigorous testing on various datasets, CBDT showcases its ability in distinguishing biased from neutral statements, while also pinpointing exact biased lexemes. Our approach outperforms existing methods, achieving a 2-4\% increase over benchmark performances. This opens avenues for adapting the CBDT model across diverse linguistic and cultural landscapes.Comment: UNDER REVIE

Arabic text detection: a survey of recent progress challenges and opportunities

The Arabic language plays a crucial role in the world after becoming the sixth official language of the United Nations (UN). In the last ten years, there has been a rising growth in the number of Arabic texts, which requires algorithmic to be more effective and efficient to represent Arabic Text (AT), detecting patterns, and classifying text into the right class. Many algorithms are available for English text, but it is not the same for Arabic because of the complexity of morphology and diversity of the Arabic dialects. This study provides a survey of research in the field of Arabic Text Detection (ATD) published from 2017 to 2023. In addition, it has been conducted in a two-fold manner. Firstly, we survey based on eleven topics related to ATD. Secondly, we survey based on three stages of ATD namely pre-processing, representation, and detection. We explore all available datasets and open sources related to AT. It is revealed through the reviewed research that there are many topics of still interest to address. Furthermore, based on our observation deep-based methods yield better results only because they comprehend both the context and semantics of the language. However, they are also slower than traditional representations. Thus, hybrid models seem to be a promising way forward. Finally, we highlight new directions and discuss the open challenges and opportunities which assist researchers in identifying future work

RESPECT: A framework for promoting inclusive and respectful conversations in online communications

Toxicity and bias in online conversations hinder respectful interactions, leading to issues such as harassment and discrimination. While advancements in natural language processing (NLP) have improved the detection and mitigation of toxicity on digital platforms, the evolving nature of social media conversations demands continuous innovation. Previous efforts have made strides in identifying and reducing toxicity; however, a unified and adaptable framework for managing toxic content across diverse online discourse remains essential. This paper introduces a comprehensive framework RESPECT designed to effectively identify and mitigate toxicity in online conversations. The framework comprises two components: an encoder-only model for detecting toxicity and a decoder-only model for generating debiased versions of the text. By leveraging the capabilities of transformer-based models, toxicity is addressed as a binary classification problem. Subsequently, open-source and proprietary large language models are utilized through prompt-based approaches to rewrite toxic text into non-toxic, and making sure these are contextually accurate alternatives. Empirical results demonstrate that this approach significantly reduces toxicity across various conversational styles, fostering safer and more respectful communication in online environments