# Biasly

Analyzes news articles and videos for bias, manipulation, and missing context.

- Category: News & Media
- Pricing: Free
- Tags: News, Research, Ethical AI Tools
- Website: https://biasly.ai/?via=aigregator
- Aigregator page: https://aigregator.com/tools/biasly
- API: https://x402.aigregator.com/v1/tools/biasly

## Overview
Biasly is a free web tool that analyzes news coverage for bias and manipulation. Users paste a URL, drop in raw text, or link a video, and the tool runs a sentence-by-sentence analysis that flags roughly 18 specific techniques, including loaded language, false equivalence, and historical revisionism. It also generates historical context for major topics like geopolitics, economics, healthcare, and civil rights, aiming to surface background that a single article often leaves out.

It's built for readers, journalists, and researchers who want a second opinion on how a story is framed rather than just whether it is true or false. Beyond text, it supports video from YouTube, TikTok, Instagram, Facebook, and X, and includes a source distribution view showing who gets quoted in a piece. No account is required to run an analysis, which lowers the barrier for casual fact-checking.

The tool is currently free during a beta period; its site says future plans likely include a paid tier for higher volume or API access, while keeping basic article analysis free. One limitation is that the underlying bias-detection model and its accuracy have not been independently verified — users should treat its labels as a starting point for judgment, not a final verdict.
## Key Features
- Sentence-by-sentence bias analysis
- Video bias analysis (YouTube, TikTok, Instagram, Facebook, X)
- Historical context generation
- Source distribution analysis

## Use Cases
- Check a news article for bias before sharing it
- Compare who is quoted across a story
- Get historical context missing from current coverage

## Alternatives
- Ground News
- AllSides

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Source: Aigregator — AI tools directory. https://aigregator.com/tools/biasly