Media Analysis Reimagined

In today's rapidly evolving media landscape, understanding how different platforms shape public discourse has become crucial for informed citizenship. Traditional legacy media outlets - newspapers, television networks, and established digital publications - operate under editorial standards developed over decades. Meanwhile, social media platforms like X provide real-time, unfiltered reactions from millions of users, creating a dynamic environment where news spreads instantly but with varying degrees of accuracy and emotional intensity. This AI-powered app monitors top global headlines and finds corresponding X posts, comparing how the same stories are covered, discussed, and received across these fundamentally different media environments. By examining sentiment patterns, emotional responses, and narrative framing, we can discover hidden patterns that influence how society processes information, forms opinions, and makes decisions. This comparative approach doesn't seek to determine which platform is "better," but rather to illuminate how each contributes to the complex tapestry of modern news consumption, helping readers develop media literacy skills essential for navigating our interconnected information age. Specifically, news stories are analyzed for overall sentiment (on a 0-1 scale where 0=very negative, 1=very positive) as well as the dominant emotion expressed, from both legacy sources and X posts. The data is then filtered for news items that are covered substantially differently and conveniently displayed in a table below (see the Latest Data section) for users to peruse, along with a commentary on the differences. The data automatically updates each day at 10pm CST (to capture a full day's worth of news stories), and is automatically posted here as well as on our X account, @Legacy_VS_X. Note that while only stories with a substantial difference in coverage are displayed here (in the latest data section), the full data for each article is in the database in case you'd like to refer to it later. See the source for further details.

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Sentiment & Emotion Analysis
Advanced AI algorithms analyze emotional tone and sentiment across legacy media and X posts to reveal hidden biases and perspectives. The latest xAI model, called grok-3-latest, provides state-of-the-art emotion detection with extreme specifity through natural language processing. As of this writing this page, it can detect ~45 emotions (e.g. joy, sadness, anger, fear, surprise, disgust, amusement, gratitude, admiration, concern, and many more), with more constantly being added.
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Real-time Tracking
Compare news stories in legacy media to X right as it unfolds, capturing the evolution of public discourse.
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Trend Analysis
Are there topics that differ significantly in the way they are presented in legacy media vs. social media? Analyzing the differences over a period of time can perhaps reveal unexpected patterns and truths. Once data ample data has been collected, I plan to write an article exploring this.

Latest Data

Comparison of world news coverage between legacy media outlets and social media discourse on X. Data is automatically updated daily at 10pm to capture evolving narratives based on a full day's worth of news headlines.

Timestamp Article Title Legacy Sentiment X Sentiment Legacy Emotion X Emotion Analysis

Archive

To access data for a previous date, please refer to our account on X, which automatically posts the current day's data at 10pm CST daily.

Explore Full Archive →

Source Code

This project is completely open source and free. It requires only PHP, MySQL, and API keys for xAI and NewsAPI. It is webserver agnostic, so you can use Apache, nginx, or whatever else will run PHP. The code that automatically posts the data to X also requires an API key from X and the free software packages wkhtmltopdf/wkhtmltoimage and ImageMagick (if you're running Ubuntu, sudo apt install wkhtmltopdf imagemagick is quick and easy).

Here is a quick rundown on the files:

View on GitHub →

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