Show HN: Identify car crash editorial anti-patterns using NLP

Hi HN,

I built "Vision Zero Reporting" (https://visionzeroreporting.com), a tool to detect editorial anti-patterns in local news coverage of car crashes.

Maybe you've noticed that local news articles about car crashes, especially those that involve "vulnerable road users" (VRU) such as bicyclists and pedestrians, tend to employ language that seems to blame the victim or only discuss the incident as an isolated event, rather than in context that crashes are preventable and are caused by specific reasons.

This tool is meant to help news publishers check their articles and learn the anti-patterns to avoid.

Here's a brief explanation of the problems my tool checks for:

1. Focus - Readers find the focus/subject of the sentence more in control of the situation, and hence more blameworthy (e.g. "A pedestrian was struck by a driver" VS "A driver struck a pedestrian").

2. Agency - Some sentences lack an agent altogether, which places more blame on the recipient (e.g. "A bicyclist was hit." VS "A bicyclist was hit by a driver.")

3. Object-based reference - Pedestrians and bicyclists are almost always referred to using people-based language, but drivers are referred to using object-based language 81% of the time [1] (e.g. "The vehicle fled the scene" VS "The driver fled the scene"). This language personifies and gives agency to vehicles rather than their drivers.

4. Accident - Accident is the most-used term in articles to describe the incident (47%). This term is being phased out by some news agencies because the word implies a sense of inevitability or that it happened purely by chance, when we know why car crashes happen and can take preventative action.

5. Framing - (still in beta) Articles employ an "episodic" frame, meaning they describe crashes as isolated incidents. Only 6% (!) of articles use "thematic" framing [1], meaning they contextualize the event by discussing road design, number of recent crashes in the area, quote experts, educate readers about road safety initiatives, etc.

6. Counterfactual - (still in beta) Counterfactuals are true statements, but imply the outcome could have been changed had the victim acted differently. While reporters may see these statements as sticking-to-the-facts, we've discovered in 700+ manually-annotated articles that counterfactuals almost always shift blame toward the victim (A bicyclist was struck; he wasn't wearing a helmet. It was dark outside, the biker wasn't wearing reflective clothing, and the driver told police he didn't see the bicyclist until it was too late.) Notice that all of these statements may be true, but goes hand-in-hand with the Framing issue discussed above: the bicyclist was hit, but is that because there is no protected bike lane? It was dark outside, but is road visibility a municipal obligation?

I'm looking for constructive feedback to make this tool better!

My work is based primarily on the following research papers (and I've already shown the tool to the authors - they loved it!):

[1] https://www.researchgate.net/publication/330975590_Editorial...

[2] https://www.researchgate.net/publication/337279845_Does_news...

This is cool! I appreciate the effort here to make the roads safer for bicyclists and pedestrians, as I'd love to see more people prioritize this.

The homepage confused me because the presentation of the two examples made me think that they were before/after at first. It took me ~30s to realize they were just two separate articles.

I'd like to see a bad article juxtaposed with an improved version that someone created using your tool.

I'm maybe used to tools like Grammarly, but when I clicked / hovered over the highlighted text, I was surprised to see nothing happen. I found it a little difficult to scroll back and forth between the highlighted text and the context where it appeared in the article. Having an explanation appear next to the cursor on mouse click/hover might resolve this.

It would also be cool if the tool allowed me to import by URL (with some suggested real articles to show this is a problem in mainstream news sources) rather than require the user to copy/paste manually.

I think this is going to be a major application for "attempting" to identify "types" of biases. I'm being super cautious here because the term bias is painfully misunderstood in most circumstances and often misused as a cudgel rather than a context. But I think it could be educational to observe cultural/social/political trends by distilling content this way.

I gather that the intent is not to make the reporting more neutral or accurate, but to change the framing in a direction that vision zero finds more appealing. E.g. in the first article we should view the woman as a vulnerable road user, bearing no responsibility for being struck by a vehicle even though video evidence shows that she fell into the street.

This could also be used for gun crime reporting as well.