This is interesting, but it seems like it is tracking stories with similar headlines and that's not always how news propagates. Frequently a blogger will read an interview, select an quote from the interview and write a new headline around the quote they cherry picked. It used to be common practice to link the original source, but that always doesn't happen.

I have long thought that search engines, news aggregators and social media companies have a journalistic responsibility to favor the original/primary source of every story, but things have not worked out that way. If you can manage to truly develop something like this it would be a valuable tool for rewarding the work of reporting over SEO.

Anyway, please consider that headlines and time stamps do not tell the entire story when it comes to sourcing.

For example: Your website offers this story (https://hotspotatl.com/6587626/dr-jackie-married-to-medicine...) as first to publish. But right in the text it cites another website BOSSIP as the source of the interview.

Also: there doesn't appear to be a way to link results from your website.

The idea is pretty cool, but it doesn't work super well. 1. I imagine most major news outlets don't have RSS feeds these days. 2. A lot of stuff originates from news agencies, so they don't spread from website to website, but radiate out from the agency. 3. Most of the included sources are pretty small. To draw meaningful conclusions we would need infos like popularity, political leaning, nation of origin, etc. 4. The similarity check doesn't appear to do translation. So when news spreads from one country to another we loose the thread.

I feel a graph diagram (hub and spoke, showing "data flow") would be a useful alt view here.

Cool website. As others note if this could tie in deep sources like FB, X, Reddit, etc...it would be almost "chain of evidence" canonical.

A view where websites/sources were associated with geo data (possibly involving a globe or map) would be very cool, too.

Without evaluating it thoroughly and judging just from description - I really hope this ends up open-sourced - will help drastically to many good-intent parties.

This is absolutely brilliant. If you integrate Reddit and Twitter/X, you’d get a much more complete picture of how stories spread across the internet.

I think the idea is interesting but it includes a lot of spam and non-news (e.g. archive.fo, .vn, .today, etc.)

How do you handle time zone issues with the dates?

I’ve been curious how much news starts from social media. So many news stories today are “someone said x on twitter”.

More important for me is how you identify news sites, let alone 200k of them. Is there any online source that lists them? Or do you cherry pick them one by one?

I'm a huge fan of the general space and I think this is a really solid approach vector to learn what user problems exist in this design.

I'll dump a few thoughts as they come for the creators, feel free to riff with me on the thread if that'll be of value.

My perspective, as a User, is I'm interested in rooting out bias and where it's coming from. Moreover, the influence networks are fascinating as well.

I think, for example, understanding which publications "picked up" a story vs didn't is very very viral use case as you could imagine people using you as a backdrop to a social post about editorial bias. That said, I think you need to pick who you serve because the folks who will be interested in this aren't the average person as they're not super news focused.

One way to learn may be looking at the types of meta-stories posted about the analysis on media and see how you could support those types of ongoing analysis. Scoring, honestly, is an another really interesting idea. What are publications "for" or "against" based on how they do editorial, and how they bias their headlines, and ledes.

Cool idea! On mobile (Chromium on Android) I was confused at first because nothing happened when I tapped any of the stories – until I realized I can zoom out and the info about how the story propagated is at the end of the page.

Kudos on releasing Yandori!

We have been (low-keep) working on something similar (more from an academic point of view) for the past few years:

This is the introductory article (open access): "Comparison of news commonality and churn in international news outlets with TARO" https://dl.acm.org/doi/abs/10.1145/3603163.3609062

(Allow me a moment of pride for the student leading this project: the paper won the Ted Nelson Award at ACM Hypertext 2023.)

Cool idea! What I liked the most was the breakdown into categories like “breaking” and “trending” plus the number of sources.

The view showing the flow with a play animation was a nice concept but I couldn’t see much value in it, wondering if you could try to get a more aggregate stats that shows a connection between these different flows, maybe they follow a pattern like ad-based campaigns or publishers who own these domains, which would explain things. Expanding on this idea, could even try and setup different scores and metrics based on major groups and sponsored content versus organic spread.

This is related to my interests!

Where'd you find all those RSS feeds? Have you done anything else with RSS feeds? :)

Also agree with the others this definitely needs interactive graphs!

That's really cool!

Curious how you sourced the feeds? It seems to have a bias towards Indian/Srilanka/Iran/Indonesia/Turkey etc - i.e. not the traditional western centric reporting. Always interested in trying to get a more balanced news diet so anything you could share around that would be interesting. Most out of the box news tools seem to automatically lean west

FYI layout sometimes breaks like so:

https://i.imgur.com/FXeqB9R.png

This seems like it could have an additional use case of labeling each news source left, right, center, neutral/factual and tracking how or if each one releases an article.

Cool idea. Given that it transferred ~29 mb when loading, is it safe to assume that the actual page is doing some of the processing? Is the front-end just doing the HNSW or is it doing the mapping of stories or headlines into vectors, or am I totally off base?

Front-end downstream of clicking on a card doesn't seem to work correctly on every reload... but it works sometimes.

Presumably a lot of large organisations have private versions of this. Are there similar projects for this that are available for private individuals, even if paid/closed-source?

Tried this on iPhone - the category tabs (Sports, World News, Business) get cut off on the right and there's no horizontal scroll indicator, so I didn't realise there were more options at first. The story cards also aren't using the full screen width, leaving wasted space on both sides.

Cool concept though - the source count and "+N" spread metrics give a quick sense of which stories have legs.

Some time ago, I wrote a scientific article in which I applied and modified the SIR model of disease spread to the spread of fake news. I simulated the whole thing in a Watts-Strogaz graph. It would be interesting to see whether the theory and formula are applicable to the real world.

I don't see news spread, eg, direct lineage graphs showing viral attribution & rewrites as a narrative propagates..

Afaict, it is the usual topic trending over time, or maybe it is showing direct sindication?

Computing actual derivation flow would be neato, esp precisely at scale vs just the usual embeddings

I dream of having that for video:

For any given clip, short or excerpt, find the most complete, unedited version that it was taken from.

It's useless to me because NONE of the titles are hotlinks, plus, you cannot even copy / paste the titles to a browser. The creator has his script set to not allowing copying.

It’s performing really slow right now. Is it possible to tell if virality of a news article is organic or manufactured? Organic is when it is produced by a reporting organization but can you see direct lineage to re-spreaders?

Really cool project — I like how clearly the flow of information is visualized. It’s interesting to see how fast certain stories propagate across networks. Curious: have you noticed patterns in which types of sources tend to spark the fastest spread?

Is there a way you could use this system to track propaganda?

Can it be tuned to get a sense of how it reach Wikimedia projects?

Very cool. Our lab will want to do something like this eventually. Do you have a repo?

Very cool. I'm curious what frontend and backend technologies are used?

I really like the idea. I would love a feature to add keywords and see related news.

I think you will need to filter out wire services like AP and Reuters, as I'm seeing stories that are mostly republished wire stories on random websites.

Just tried it, and clicking on the stories doesn't seem to do anything. Console shows "TypeError: can't access property "time", flowData[Math.min(...)] is undefined"

Ubuntu 24.04, Firefox 145.0.1 (64-bit)

Good idea. Clean execution. Nice UI. I will repeat other poster's plea to make it open source. The information this provides is useful.

great !

See also Newscord, which does very similar work to analyze bias across news media:

- https://newscord.org/latest

- https://www.instagram.com/newscord_org

This looks a lot like a combination of spam and slop posed as "breaking news".

> Opinion: Operation Holiday serves a critical need in our communities

> Dhru Fusion WooCommerce Integration Plugin

> Powering the Future of Wellness Through Premium Food Supplement Ingredients

That isn't even remotely important at all so really unreliable.

Is there something similar that could be built to track spreading across social media? For example to track misinformation and its patterns? Or is that no longer possible because of changes to the Twitter API or whatever?

I feel like there's a huge necessary civil virtue to this sort of understanding the news project.

Thanks for sharing some details. Its cool that HNSW is useful for near realtime usage. For some reason I had categorized it in my head as having very very high insertion cost, needing to rebuild worlds to work but that's not at a well founded belief; very cool that it's usable here.

I really hope we see some open source work of this variety. Trying to understand news or even social media is something the world seems to unprepared for. Different subject sort of, but watching Internet Observatory be dismantled by the current political administration, by disinformation grifters, was a woeful loss of one of the few mirrors the that humanity had to understand itself with, to see how we networked.

Interesting project - it’s rare to see news-flow tracking done in real time at this scale. One thing you may want to stress-test is how stable the clustering remains when stories evolve semantically over a few hours. Embeddings tend to drift as outlets rewrite or localize a piece, and HNSW can sometimes over-merge when the centroid shifts.

A trick that helped in a similar system I built was doing a second-pass “temporal coherence” check: if two articles are close in embedding space but far apart in publish time or share no common entities, keep them in adjacent clusters rather than forcing a merge. It reduced false positives significantly.

Also curious how you handle deduping syndicated content - AP/Reuters can dominate the embedding space unless you weight publisher identity or canonical URLs.

Overall, really nice work. The propagation timeline is especially useful.

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