🚧 midlyne is under development, we have reverted it back to an older version for now while we fix a few things. Bringing unbiased media transparency to you soon... 🚧

midlyne

Bringing transparency to Indian news coverage through multi-source analysis and dimensional bias visualization.

What is Midlyne?

Midlyne is a media transparency platform that aggregates news from multiple Indian outlets and analyzes how different sources cover the same stories. Instead of presenting a single narrative, we show you the full spectrum of coverage.

Our system automatically clusters related articles from different news sources, identifies the key dimensions of framing and bias, and visualizes where each outlet stands on various perspectives.

How It Works

1

Multi-Source Aggregation

We continuously monitor major Indian news outlets, collecting articles as they're published to ensure comprehensive coverage.

2

Smart Clustering

Our AI identifies when multiple outlets cover the same event or story, grouping related articles into unified topics.

3

Dimensional Analysis

Each article is analyzed across 14 bias dimensions, revealing how different sources frame the same news differently.

4

Visual Transparency

Interactive visualizations show you exactly where coverage falls on each dimension, making bias visible at a glance.

Understanding Dimensions

Midlyne analyzes coverage across 14 bipolar dimensions. Each dimension represents a spectrum of framing choices journalists make when covering news. A score closer to either pole indicates stronger framing in that direction.

Factual
Sensational
How dramatically is the story presented?
Left
Right
Political leaning in coverage framing
Pro-Labor
Pro-Business
Economic perspective on work and enterprise
Secular
Religious
Role of faith in the narrative

Sources We Monitor

We aggregate from 13+ major Indian news outlets across the political and editorial spectrum to ensure diverse coverage.

Our Commitment to Transparency

Midlyne is itself a work in progress. Our bias detection models are continuously being refined, and we're transparent about their limitations. We don't claim perfect objectivity, we aim to make the biases that exist in news coverage visible so you can be a more informed reader.