Our Core Approach
- LLMs Trained on Diverse Data: We use large language models to recognize nuanced linguistic patterns far beyond simple keywords.
- Social-Science Foundations: Concepts from psychology and linguistics guide how we detect framing bias, confirmation bias, selection bias, and emotional appeals.
- Fast & Scalable Pipeline: Our infrastructure can analyze anything from a few paragraphs to large corpora in seconds, with consistent outputs.
Analytical dimensions
- Framing: Shows how language, emphasis, and presentation shape interpretation.
- Emotional Language: Identifies emotional signals such as fear, outrage, urgency, sympathy, distrust, and moral condemnation.
- Evidence & Certainty: Explores certainty, uncertainty, speculation, ambiguity, and evidential grounding throughout the text.
- Bias & Influence Indicators: Reveals framing bias, selection bias, confirmation bias, emotional appeals, and other potential sources of influence.
Why You Can Trust the Method
- Explainable Outputs: Reports include a bias meter, stance breakdowns, and category-level rationales to clarify why something was flagged.
- Scientifically Informed: Our criteria are inspired by experimental psychology and linguistics, adapted for robust, digital-at-scale analysis.
- Consistent & Automated: No human reviewers influence outcomes; the same input yields the same analysis, enabling reliable comparisons.
- Critical-Thinking First: AI augments judgment—it doesn't replace it. Use results as decision support alongside expert and editorial review.
- Built by IntelAnvil: Check Text Bias is developed by IntelAnvil and showcases the analytical methods and AI systems that power our broader work in information analysis and decision support.