Scoping Review of Natural Language Processing: Traditional and AI-Based Techniques in News Media and Mental Health

In recent years, the intersection of traditional and artificial intelligence (AI)-based Natural Language Processing (NLP) techniques has emerged as a significant area of interest, especially in the domains of news media and mental health. This article explores key findings from a scoping review that examines these NLP techniques applied to these critical spheres, highlighting their relevance to addressing misinformation and mental health issues.

Grouping of NLP Techniques

NLP can be broadly categorized into two groups: traditional content analysis methods and AI-based approaches. Traditional methods often involve manual coding and thematic analysis, which are essential for qualitative insights, albeit labor-intensive. In contrast, AI-driven techniques leverage large datasets to generate insights across broader contexts almost in real time.

Traditional Content Analysis

Traditional content analysis methods form the backbone of media studies in psychology and social sciences. These approaches offer structured frameworks for understanding how mental health is portrayed in various media texts.

• Qualitative Content Analysis: This method focuses on systematically interpreting textual data through classification of themes. For example, researchers manually code texts based on predefined rules, ensuring nuanced interpretations.

• Quantitative Content Analysis: This method quantifies specific elements within the text, using statistical techniques to identify trends. Although highly objectified, it may overlook deeper meanings and context.

In many cases, a mixed-methods approach is adopted, blending both qualitative and quantitative techniques to provide a holistic view of the subject matter. This combination enables researchers to validate findings across both qualitative nuances and quantitative trends.

NLP Applications in News Media

Examining NLP applications in news media shows that research within this domain has surged, particularly from 2021 and 2022, mainly due to increasing awareness of the impact media coverage has on public perceptions of mental health.

Benefits of NLP for Media Analysis

NLP has become an invaluable tool for dissecting vast amounts of textual content generated by traditional and digital news outlets. Its capabilities include:

• Sentiment Analysis: This technique assesses the emotional tone of media narratives, revealing potential biases that can contribute to stigma surrounding mental health.

• Topic Modeling: NLP can extract recurring themes that influence public perceptions, such as the frequent association of mental illness with crime or social instability.

• Misinformation Detection: During events like the COVID-19 pandemic, the ability to identify and counter misinformation became crucial, enhancing public understanding of mental health issues.

Structure and Framing in News Reporting

Traditional news articles adhere to professional journalistic standards, providing balanced and structured information. This contrasts sharply with informal, subjective content typical of social media. Understanding these linguistic and structural distinctions is crucial for applying NLP techniques effectively.

Research Trends

The increasing use of transformer-based models such as BERT and GPT has revolutionized how researchers conduct media analysis. These models excel at understanding complex narratives and long-range dependencies in text, creating a more comprehensive analysis of mental health portrayals.

NLP’s Role in Mental Health

The role of NLP in mental health has grown, focusing on analyzing linguistic patterns associated with various mental health conditions. By leveraging these NLP techniques, researchers can:

• Identify Emotional States: NLP can assess mental health risks by analyzing language use in social media contexts.

• Personalize Interventions: By analyzing communication in crisis situations, NLP can help tailor mental health support to individual needs.

• Extract Psychopathological Cues: NLP models have shown promise in identifying relevant indicators from therapy transcripts, thus facilitating more accurate assessments.

Bridging Gaps in Mental Health Research

Despite advancements, several gaps persist:

• Language Bias: Many NLP applications focus predominantly on English-language datasets, limiting accessibility and inclusivity.

• Model Transparency: Understanding how NLP models arrive at their conclusions requires enhancement in interpretability.

• Ethical Considerations: Ensuring data integrity and ethical use remains critical in deploying NLP tools for mental health.

Recommendations for Future Research

1. Diverse Datasets: Developing diverse, representative datasets is essential for generalizing NLP models accurately across contexts.

2. Bias Mitigation: Implementing strategies to identify and reduce biases in training data is critical for ensuring equitable outcomes.

3. Model Validation: Rigorous testing of NLP models is necessary to ensure reliability and accuracy in real-world applications.

4. Leveraging Pre-Trained Models: Utilizing domain-specific models can enhance analysis relevance, particularly in sensitive areas such as mental health.

5. Multi-Dimensional Analysis: Developing comprehensive analytical pipelines that incorporate various NLP tasks will allow for richer insights.

6. Improving Interpretability: Enhancing transparency in model decision-making processes will enable better understanding and trust in NLP applications.

7. Focus on Linguistic Diversity: Designing NLP techniques that accommodate multiple languages and dialects addresses current biases in data representation.

8. Enhancing Reproducibility: Standardizing metrics and improving data and code availability will foster transparency and replicability in NLP research.

9. Multimodal Approaches: Integrating text, audio, and visual data will provide deeper insights into mental health and media representations.

10. Ethical AI Practices: Prioritizing ethical considerations in the development of NLP applications ensures they serve the broader public good.

By implementing these recommendations, future research can solidify NLP’s role at the intersection of news media and mental health, fostering informed discourse and nuanced understanding.