Unraveling Media Perspectives: A Comprehensive Methodology Combining Large Language Models, Topic Modeling, Sentiment Analysis, and Ontology Learning to Analyse Media Bias (2505.01754v1)

Abstract: Biased news reporting poses a significant threat to informed decision-making and the functioning of democracies. This study introduces a novel methodology for scalable, minimally biased analysis of media bias in political news. The proposed approach examines event selection, labeling, word choice, and commission and omission biases across news sources by leveraging natural language processing techniques, including hierarchical topic modeling, sentiment analysis, and ontology learning with LLMs. Through three case studies related to current political events, we demonstrate the methodology's effectiveness in identifying biases across news sources at various levels of granularity. This work represents a significant step towards scalable, minimally biased media bias analysis, laying the groundwork for tools to help news consumers navigate an increasingly complex media landscape.

• The paper presents a novel methodology for media bias analysis integrating Large Language Models, topic modeling, sentiment analysis, and ontology learning.
• This scalable framework leverages advanced NLP techniques to objectively examine media coverage, reducing reliance on subjective human assessment.
• The study provides a foundation for future research, including enhancing ontological coherence and extending analysis to diverse media sources and languages.

Expert Overview of the Methodology for Analyzing Media Bias Using AI

The paper, "Unraveling Media Perspectives: A Comprehensive Methodology Combining LLMs, Topic Modeling, Sentiment Analysis, and Ontology Learning to Analyse Media Bias," presents an innovative approach for systematically examining media bias in political news reporting. This paper demonstrates how the integration of advanced NLP techniques, including topic modeling, sentiment analysis, and ontology learning, can be effectively utilized to identify and assess media biases across various levels of granularity and media sources.

Key Methodological Approaches

1. Hierarchical Topic Modeling: The paper employs BERTopic, a sophisticated neural topic modeling technique, to discern topics within a corpus of news articles. This model provides insights into how different media outlets may selectively report on events, revealing potential overlaps or omissions in story coverage. The hierarchical aspect of BERTopic allows researchers to adjust the thematic granularity, facilitating both broad and nuanced analyses of media coverage.
2. Sentiment Analysis: Two stages of sentiment analysis are conducted using RoBERTa and spaCy models. RoBERTa, fine-tuned for news sentiment, analyzes article titles to ascertain immediate emotional tones, while spaCy's sentiment analysis measures sentiment within article bodies. This differentiation between title and body analysis helps in capturing the distinct biases often present in sensational headlines versus the more neutral language found in comprehensive text analyses.
3. Named Entity Recognition (NER): The paper utilizes NER to identify and categorize entities within articles. This process, using BERT-based models, is essential for understanding how often specific individuals, organizations, or locations are referenced and whether these references carry positive, negative, or neutral sentiments.
4. Ontology Learning: Through the use of LLMs like GPT-4, the paper engages in ontology learning to develop structured representations of knowledge extracted from news articles. These ontologies facilitate understanding the connections between different entities and concepts within the media narratives, offering a framework for detecting biases based on exclusion or emphasis of particular facts or viewpoints.

Implications and Future Directions

The proposed methodology presents several practical and theoretical implications for the field of media analysis:

• Scalability and Efficiency: By leveraging machine learning techniques and reducing human input to HITL processes, the methodology offers a scalable solution to analyzing vast datasets of news articles. This scalability is vital for tracking media bias across multiple languages and regions.
• Reduction of Human Bias: The methodology minimizes subjective bias through its reliance on algorithmic processing, providing a more objective baseline for media analysis compared to traditional qualitative assessment methods heavily reliant on human judgment.
• Potential Enhancements: Future work suggested by the paper includes improving ontological coherence by standardizing semantic representations and enhancing the visualization tools for more intuitive result interpretation. Moreover, expanding the context range for sentiment analysis holds promise for capturing more nuanced biases embedded in article narratives.

This paper signifies a meaningful advancement in applying AI and NLP to the critical social issue of media bias, offering both a comprehensive methodological framework and a launching pad for further innovation in this area.