Federer with the steepest curve

Although I hear about them all the time, I've never met a person in real life who uses the reddit app, or something similar. I know lots of redditors, and they all use desktop.

Looks like Canada and Australia hit way above their weight on that.

Don’t quite understand the color scheme.

Got it thanks for the reply! I know not everyone supports RSS, and it’s a challenge when folks format RSS in different ways, but as they’re a primary source from the publisher I’d encourage you to use RSS over APIs from Google.

I was curious the signals in your algorithm as well. One of the challenges with automating taxonomies for news stories is the inexactitude of language and differences in style. A story might mention DeSantis and books in the headline and description but might actually be about GOP primaries; a story might emphasize DeSantis in the primaries in the headline and title but it might actually be about book banning.

Or a better example: a story that mentions Tyre Nichols may be about the actual incident, police violence or defunding the police.

Digging in even further, a local news organization might use colloquialisms for place names that can make it difficult for folks from outside that market to categorize those stories.

Really bad division between regions, different scale in graphs makes much harder comparison between different regions.

Also the title seems to imply causation and let's be honest, the amount of pharmacists has a pretty low impact on life expectancy, the just handle out simple medicines or what the doctor prescribed, having doctors per capita would definitely be a much better comparison

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Data source: NSIDC Tool: Excel

Source: wiki articles:

• Grand slam winners
• Big three: https://en.wikipedia.org/w/index.php?title=Big_Three_(tennis)

Tools: datawrapper, photopea

Edit: link to wiki articles added Edit2: formatting

Do you have a link to that data, too? Because the one you posted in another comment (https://www.theglobaleconomy.com/rankings/pharmacists_per_1000_people/) doesn't correlat to anything shown in your graphs at all. There is only data from 2 Asian countries (South Korea and Israel), 20 European ones, as well as Canada and Australia and New Zealand. And the values range from 0.2 to 1.3 not in the double digits like in your graphs.

I'm getting the data from the Google News API. I've used RSS feeds in the past with similar results.

And actually I'm using a clustering algorithm to identify the specific stories. I have an automated process that pulls all articles from the past five days, clusters them into stories, then produces a bunch of analysis. This saves me a lot of time and brings some objectivity to the process.

Ok I gotcha. I'm a little slow on the uptake since I'm just getting into this. I'm now trying to figure out the best way to get the annual averages in my chart. Anyway, thanks for all the guidance.

Categories came from the data.

I seem to be having trouble communicating here. Absolutely, dollar cost averaging is the way to go. The other stuff I'm suggesting, I'm suggesting because it's worse, and if you want to show how good dollar cost averaging is, you need to compare it to something that is worse, but that somebody naive might do. I'm suggesting these for your plot, not for your investing strategy. For investing strategy, dollar cost averaging is the way to go, unless you are a genius or you have a better understanding of the particular market and company, then is available to expert investors.

What kind of categories are that?

You divide Asia up between Eastern Meditteranean (btw. Morrocco is as west as you can get in the mediterranean (even more than Algeria, who you put into Africa), but ok), South East Asia (why tf is one of the Koreas in that category?) and Western Pacific, but then lump Uzbekistan and Turkmenistan in with Europe? Also, how tf is Cambodia Western Pacific, but Indonesia and Timor-Leste are not? Have you ever looked at a map?

I just looked up Value Averaging and it suggests investing more when the stock is going down and less when it's going up. I'm curious about your approach.

Happy to help! And I think you’re spot on when you say you need to clarify the definition of prevalence. Just because a news org puts resources into a topic doesn’t mean it’s prevalent to the user. That said, the number of stories a news org efforts on a subject is an interesting data point.

As someone on the other side of this, I hear you on the challenges associated with getting useful data. How are you currently tracking all articles published by those news orgs? And how are you parsing that data to identify specific stories - what search terms are you using to filter the data?

Number is the percentage chance of the next letter being the column letter, assuming you're already at the row letter. I.e. if you are at a "Q", there's a 95% chance the next letter will be a "U". This says nothing about the "popularity" of the letter combination by itself.

"A" as a next letter follows most consonants. Not necessarily the second letter in the name.

This is an excellent comment, thank you for this!

I think I need a clearer way of describing "prevalence". This chart is showing the top ten stories by the share of articles written about them, not by the amount that they are consumed. I take articles from 64 sources on every day, cluster them together into "stories", then calculate each story's share based on the number of articles written about it. For example, if there are 1000 articles for a day, and one story has 100 articles written about it, then its share is 10%. Does that make sense?

I've explored measuring consumption of news in the past, and found it to be very difficult! (Facebook's Graph API used to be wide open, so I was able to get likes/engagement on news stories there, but it has since been locked down) Your comment does a great job of explaining the complexity in measuring consumption. You would need to combine:

- GA data from news outlets (which they don't publish)

- Cable news data (sources exist for this, but you would need to make a lot of assumptions to combine this with articles)

- Social media data

And you would need to make a lot of assumptions about what weights to use on each of those. As a result, I'm keeping this simple and focusing on article shares.

I do publish a daily automated Twitter thread on which news outlet gets the most engagement on Twitter. It includes the most liked and ratioed tweets from each "side" of the media. This is limited to Twitter, so does not cover all the channels you described. See an example here: https://twitter.com/PartisanPlayG/status/1619300675094970369

The other thing I've been doing is cutting articles by which "side" of the media they're on using media bias ratings from AllSides. Again, this involves some simplifying assumptions so it's not perfect but gives a good high-level view. You can see examples here: https://partisanplayground.substack.com

Thanks again for your comment. This is exactly the sort of thing I was looking for when I posted.

A Chinese invention banned in China?

"Ironic." (Cit not Darth Sidious)

Please recheck the graphs again.

Reddit is banned in China, and they also have their own version of Reddit, called Tieba

Nope. You are wrong. They are called zoomers because they go from A to Z so quickly... They are already at Z.

Always on the move, zipping and zapping, zigging and zagging. Zoomers.... Faster than a Yoomer.

The burger flipper can buy a house if they go freelance and get paid per flipped burger. They'd zoom through it and increase their earnings significantly. Or they could set up a spatula subscription service where people will receive a spatula whenever they need to flip a burger. A corn starch plastic disposable spatula.

You were thinking about this a lot but never thought that their might be regulations on how a car can look and function like? Just for starters, not like their are hundreds of other reasons why a team shouldn't do it like.... trying to get as many points as possible and not relying on the fact that 10 cars need to DNF in order to get into the points.

We have a saying in broadcast news. When you’re tired of talking about it is usually when the audience is first hearing about it.