How Source Bias Analysis Works
Our bias analysis pipeline processes every article that mentions your searched entity through a multi-stage natural language processing system. First, articles are collected from our index of 50+ news sources spanning major English-language Indian outlets, regional publications, and international media with India desks. Each article is matched to an entity using named entity recognition, ensuring that only genuinely relevant coverage is included.
Next, each article undergoes political framing analysis. Our AI models evaluate the language used in headlines, opening paragraphs, and body text to determine whether the framing favours left-leaning, centrist, or right-leaning political narratives. This is not a simple keyword lookup. The models are trained on thousands of Indian news articles that have been manually annotated by media analysts, allowing them to recognise subtle framing choices such as the difference between calling a policy "relief for the poor" versus "populist spending."
Sentiment analysis runs in parallel, scoring each article on a 0-to-100 scale that measures the overall tone of coverage toward the searched entity. A score above 55 indicates predominantly positive framing, below 45 indicates negative framing, and scores in between represent neutral or mixed coverage.
The per-source averages you see in the results are computed by aggregating all articles from a given outlet within the selected time window. This means a source's bias score is entity-specific: the same outlet may score differently for different politicians or topics, reflecting actual editorial choices rather than a static label.
Time-window selection is important because bias can shift during breaking news cycles. A source that typically provides centrist coverage may lean significantly during election season or a major policy debate. By comparing 24-hour, 7-day, and 30-day windows, you can see whether a source's leaning on a topic is a momentary reaction or a persistent editorial stance.