The election is almost a week away, and the people demand to know what will happen. This, as always, leads to a lot of polls and a lot of frantic speculation. One problem with this, though, is that a lot of people out there are passive consumers of news. They read what happens to cross their feed, and so what they see will be a function of what goes viral. The problem with this is that the most viral polls are not the most representative polls. I explored this in the old Twitter days, but I thought it was worth revisiting now.
Given that Musk has absolutely destroyed the Twitter API for academic use, I had to instead rely on what happens on Bluesky. Bluesky is well known for having a clear left-wing tilt, while also being the posting retirement home for elder millennials. So this is not exactly a representative sample of what goes viral in polling, but it is in line with what I found a long time ago looking at Twitter. In brief, I scraped the last 90 days of posts from @usapolling.bsky.social, who appears to be the most active “polling distribution” account on Bluesky. After that, I used an LLM and an overly complicated prompt (see below) to extract the information in each post. I then calculated the margin for the race(s) in each post and finally calculated the average margin for each race (Dem-Rep). To further simplify things, this analysis focuses only on posts that included a single general election poll. Nothing is perfect, but I probably should be spending time working on publishable research and lectures anyway.
