Okay, so I was messing around with large language models the other day, and I got this wild idea: could I use them to figure out if news sources are biased? I mean, we all kinda know some sources lean one way or another, but could I get some data to back that up?
The Plan: I decided to use the rmg package (yeah, I know, sounds kinda shady, but it’s just a Python thing) to grab articles from a bunch of different news outlets. Then, I’d feed those articles into a language model and see if it could detect any political slant. Seemed simple enough, right?
Step 1: Gathering the News: First, I had to pick my news sources. I tried to get a mix – you know, some that are generally considered left-leaning, some right-leaning, and some that claim to be neutral. I won’t name names here, don’t wanna start a war in the comments. Then I used some basic scraping and the rmg library to just pull down a bunch of articles. This was honestly the most tedious part. Lots of cleaning up HTML and getting rid of all the ads and junk.
Step 2: Choosing the LLM: This is where things got interesting. I experimented with a few different large language models, both open-source and some of the bigger, commercial ones. I tried to use a smaller model at first, but it didn’t seem to be sophisticated enough to really pick up on the nuances of political language. So, I ended up using a more robust (and expensive!) model. It cost me a little bit of money to do it but, hey, the knowledge and experience is priceless.
Step 3: Prompt Engineering: Okay, so just feeding the articles into the LLM and asking “Is this biased?” didn’t work so well. I had to get a little more creative. I ended up crafting a prompt that asked the model to rate the article on a scale from “Very Left-Leaning” to “Very Right-Leaning,” and to give a brief explanation for its rating. This seemed to give me more consistent and useful results. It took some trial and error, tweaking the wording of the prompt until I felt like it was actually doing what I wanted.
Step 4: Running the Analysis: Once I had my prompt dialed in, I just ran all the articles through the LLM. This took a while, even with a pretty powerful machine. I had to chunk the articles into smaller pieces because the LLM had a limit on how much text it could handle at once.
Step 5: Looking at the Results: This is where it got really interesting. The LLM did seem to pick up on some pretty clear differences between the news sources. Some sources consistently got rated as more left-leaning, others as more right-leaning. And the explanations it gave for its ratings were actually pretty insightful. It would point out specific phrases, arguments, or sources that it identified as indicative of a particular political viewpoint.
What I Learned:
- LLMs can definitely be used to detect political bias in news articles.
- Prompt engineering is key. You have to be really specific about what you want the model to do.
- It’s not a perfect science. There’s still a lot of subjectivity involved, and the LLM can definitely make mistakes.
- This is just a starting point. There’s a lot more research that could be done in this area.
Caveats: Look, I’m not saying this is the definitive answer to whether or not a news source is biased. This was just a quick experiment. But it was a really interesting one, and it gave me a lot to think about. Always be critical of what you read, and don’t just blindly trust any news source, no matter how “objective” they claim to be.
