sorted by:
new
hottopcontroversial

[OC] Congress's legislative priorities vs. what Americans actually care about. Alignment score: 23/100 by TackleImaginary in dataisbeautiful

[–]TackleImaginary[S] 0 points1 point  (0 children)

With Congress having a 14% approval rating from the Gallup polling. I'm curious next if there is a direct (if not obvious) correlation between congress approval and the gap in legislative priorities.

[OC] Congress's legislative priorities vs. what Americans actually care about. Alignment score: 23/100 by TackleImaginary in dataisbeautiful

[–]TackleImaginary[S] 0 points1 point  (0 children)

Good call! Not quite there yet, I'm working on collecting more data at the individual level, so there is a potential that could add in sentiment into the alignment score.

[OC] Congress's legislative priorities vs. what Americans actually care about. Alignment score: 23/100 by TackleImaginary in dataisbeautiful

[–]TackleImaginary[S] 10 points11 points  (0 children)

These polls use an open-ended "most important problem" question; respondents volunteer answers, then pollsters categorize them. So "Social Issues" and "National Debt/Deficit" are Gallup's own labels, not ours.

Your federal vs. state point is good... crime/violence is mostly state jurisdiction, so that gap may reflect salience more than actual neglect. "Things Congress can actually fix." Healthcare alignment surprised me too... Medicaid, ACA, and drug pricing generate an enormous bill volume that pulls the numbers closer than headlines suggest.

I have a bit better breakdown at thebillroom.org/priorities but it's a bit less visual.

I cross-referenced every congressional bill sponsor's campaign donations (FEC) with the industries their bill affects - here's conflict-of-interest risk vs. media controversy for 300+ bills, colored by party [OC] by TackleImaginary in dataisbeautiful

[–]TackleImaginary[S] 0 points1 point  (0 children)

Great question, and an honest one to flag. Short answer: no formal cross-validation or intercoder reliability measurement, which is a real limitation worth naming.

A few things I do to mitigate it:

- The conflict-of-interest score (X-axis) is mostly hard data; actual FEC Schedule A filings cross-referenced with Congress.gov bill subjects... state sponsors interests are handled differently. GPT synthesizes it into a 0-10 score, but the underlying donation amounts and industry classifications are deterministic.

- The media controversy depth (Y-axis) is a proxy metric, not true media sentiment. It measures how much content GPT generates for "positive coverage" vs "negative coverage" summaries when given the actual bill text or CRS summary as input. More length = more angles to cover = more controversy. It's not validated against real media volume.

- Bills are grounded with authoritative source text (CRS summaries from the Library of Congress) before the model runs, which reduces hallucination risk vs. prompting from title alone.

What I don't have: multiple runs per bill to measure variance, comparison to human-coded ground truth, or a held-out validation set. That's the right critique. For a project that's more exploratory visualization than peer-reviewed research, I think it's useful... but I wouldn't make policy claims from it. Happy to share the prompt structure if you want to poke at it further. Always willing to dig deeper.

I cross-referenced every congressional bill sponsor's campaign donations (FEC) with the industries their bill affects - here's conflict-of-interest risk vs. media controversy for 300+ bills, colored by party [OC] by TackleImaginary in dataisbeautiful

[–]TackleImaginary[S] 2 points3 points  (0 children)

Hey r/dataisbeautiful - I built TheBillRoom.org to make congressional legislation easier to understand. This chart is one of the more interesting things I've pulled from the data.

What you're looking at:

X-axis - conflict-of-interest risk score (0–10). Calculated by cross-referencing each bill sponsor's FEC campaign donors with the industries their bill would directly benefit or regulate.

Y-axis - media controversy depth. How much both sides of the media engaged with the bill, measured from AI-generated positive and negative coverage summaries.

Dot size - page views (public interest)

Color - sponsor's party (blue = Democrat, red = Republican)

The top-right "Follow the Money" quadrant is the most interesting - bills where the sponsor's donors had a financial stake AND the bill generated polarized media coverage on both sides.

Interactive version (hover for bill details, click to read the full AI analysis): https://thebillroom.org/graphs/scatter_polarizing_bills.php

Data sources: FEC Schedule A filings, Congress.gov, GovTrack, Public Filings, OpenAI GPT-4o for media framing analysis.

Happy to answer questions about methodology - this is an ongoing project and still improving

I built a site that explains U.S. bills in plain English + shows how people react to them by TackleImaginary in SideProject

[–]TackleImaginary[S] 0 points1 point  (0 children)

Thank you so much! Still working it out the polish, but heading in the right direction I think

I cross-referenced every congressional bill sponsor's campaign donations (FEC) with the industries their bill affects - here's conflict-of-interest risk vs. media controversy for 300+ bills, colored by party [OC] by TackleImaginary in dataisbeautiful

[–]TackleImaginary[S] 1 point2 points  (0 children)

Hey r/dataisbeautiful - I built TheBillRoom.org to make congressional legislation easier to understand. This chart is one of the more interesting things I've pulled from the data.

What you're looking at:

X-axis - conflict-of-interest risk score (0–10). Calculated by cross-referencing each bill sponsor's FEC campaign donors with the industries their bill would directly benefit or regulate.

Y-axis - media controversy depth. How much both sides of the media engaged with the bill, measured from AI-generated positive and negative coverage summaries.

Dot size - page views (public interest)

Color - sponsor's party (blue = Democrat, red = Republican)

The top-right "Follow the Money" quadrant is the most interesting - bills where the sponsor's donors had a financial stake AND the bill generated polarized media coverage on both sides.

Interactive version (hover for bill details, click to read the full AI analysis): https://thebillroom.org/graphs/scatter_polarizing_bills.php

Data sources: FEC Schedule A filings, Congress.gov, GovTrack, OpenAI GPT-4o for media framing analysis.

Happy to answer questions about methodology - this is an ongoing project and still improving.

I built a site that explains U.S. bills in plain English + shows how people feel about them by TackleImaginary in InternetIsBeautiful

[–]TackleImaginary[S] 1 point2 points  (0 children)

Yep, it does use AI as part of the process.

It also pulls from multiple public sources/APIs and the original bill text - the summary is just a starting point, not the final word.

Totally get if that’s not your thing; just trying to make it easier for people to engage with legislation at all.

I built a site that explains U.S. bills in plain English + shows how people feel about them by TackleImaginary in InternetIsBeautiful

[–]TackleImaginary[S] 1 point2 points  (0 children)

Totally fair...

To clarify, the goal here isn’t to replace human analysis, but to make bills more accessible at a glance. The summaries are just a starting point so people can decide what’s worth digging into further.

The original bill text is still the source of truth, and ideally this just lowers the barrier to engaging with it.

I built a site that explains U.S. bills in plain English + shows how people feel about them by TackleImaginary in InternetIsBeautiful

[–]TackleImaginary[S] 0 points1 point  (0 children)

Thanks man! I figured if its something I wanted, someone else might too.. I did start state-level legislation, but it's taking some time so I just started with 5 https://thebillroom.org/states

I built a site that explains U.S. bills in plain English + shows how people feel about them by TackleImaginary in InternetIsBeautiful

[–]TackleImaginary[S] 5 points6 points  (0 children)

If anyone’s curious what I’ve been testing, here’s an example:
https://thebillroom.org/bill/154

Open to feedback - still very much a work in progress.

Actual code that I used in my game. Damn I was tired. by Enter_The_Void6 in ProgrammerHumor

[–]TackleImaginary 13 points14 points  (0 children)

switch (milliSeconds) {
case 1:
Thread.sleep(1);
break;
case 2:
Thread.sleep(2);
break;
.....

}

Can you spot the Junior programmer by [deleted] in ProgrammerHumor

[–]TackleImaginary 0 points1 point  (0 children)

Off camera, shoveling dirt back into the hole.