dev.to is a wonderful blogging platform that emerged a few years ago. I love writing for it and reading content published there. But what I like th...
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One should check for one more fallacy: what if the same great authors always post at the same time and the author is the deciding factor for number of likes and not time?
Two methods come to mind:
Yes, you are totally right.
I think this kind of fallacy can also be caught by measuring the median number of like across the day instead of mean. I did it and did not find that much difference in the results.
I'll try your point 1, haven't thought of this one though.
And I plan to make an article about "famous" poster and their stat that will deal about point 2.
If we assume the opposite of "some people are great writers", namely "most people are bad writers", then there might be a situation where the majority make not good articles that aren't affected by time of day. In such a scenario using the median would be less telling.
That said, most posts here are pretty well-made, so that shouldn't be a problem. If it were the case, then the median would br pretty much even across all times of day, so if you didn't see much difference, then that disproves it.
If we think that "great authors" post at the same time then, even if we cater for numbers of users, we're basically assigning qualities to longitudes. One uncausal correlation from that would tell us we'd get more likes if we moved to a different country.
I think this is a whole lot like the rocket equation. The more people try to target "best times", the more they'll skew the data away from themselves.
Nice catch there...
Very cool! You can probably refactor it to use the "paged" articles. This will give you a set of 30 public article IDs per page so you don't have to scrape 94k IDs directly. You can just loop through the ~500 pages
https://dev.to/api/articles/?page=468to grab the IDs. A cool thing about dev.to is since it's open source the API may not be documented but is available to view github.com/thepracticaldev/dev.to/.... Can't wait to see more!The articles api seems to return only articles with the defaults tags career discuss productivity
github.com/thepracticaldev/dev.to/...
I'd have to double check but I believe that's only during "onboarding" when a user first signs up. If not that's likely a bug in my mind.
I've done a runkit how check if the most recent react article is published after the most recent article, if so, check if it is in the latest 30 articles.
runkit.com/alfredosalzillo/5c9ba0d...
The react article, published after the most recent article (of the dev.to/api/articles/) isn't in the articles list.
It may be a bug of the api.
Thanks, did not know about this endpoint. Will use it for my next one.
Very nice meta article!
I did a similar job on where are dev.to users coming from :
🌍 Where Are DEV Users Coming From?
🇫🇷 Boris Jamot
Wow, I’m definitely going to read this when I get a chance. Skimmed it.
Initial thoughts are that it’s definitely affected by when we are awake and working because of how we schedule for Twitter etc. But we’re always evolving and modifying the process so this could change.
I can’t wait to read through this.
Thank you! I hope you'll find helpful insights.
Love this post! ❤️
Just throwing this out here, but ya might want to switch out one of your tags for #meta as this is definitely an option - dev.to/t/meta ... however, all of your tags are apt descriptions.
Again, totally digging this post!
Thank you very much!
Great tag suggestion, just added it.
Really nice article.
A question though, shouldn't it include the age of articles in the factors to check?
Not sure it has a big impact but an older article would potentially get more likes than a recent one.
While this concern sounds right, after thinking about it I think it is not much of a problem because:
1: By observation, I noticed that articles tend to only gather positive reactions during the first 4 days of posting
2: Even if that would not be the case, if we assume that the pattern had not changed in three years, then there are no problems giving more weight to older posts.
Those two assumptions do not seem very far-fetched.
And again, if we assume old post = more likes, we can always do the same heatmap with a median, which gives almost the same results.
A valid concern.
These tables tell me a different story.
Here is a serious question: Are these results persistent across weeks and across months?
That is an interesting question,
I thought about making a gif of multiples heatmat across day / time / month hoping to discovere change of habit.
I'll post about it results are showing something interesting.
I know this is an old article but I'm wondering - have you got around to do the comparison? I'm pretty sure there would be a noticeable difference due to daylight saving time shifts, if nothing else.
Hmm.. One further analysis might be the use of tags. You can analyse the average tags on reactions per article and time of publishing.
This will be useful as well cause I based my articles on the tags that I would use which usually is the top 10-20 tags to optimise the numbers of reach and view for me.
Thanks for your idea, I actually wanted to do this for my next one.
Fun project, coincidentally I did a heatmap project myself yesterday too.
I've written a discord bot for a server, and it stores the amount of messages sent per hour, so I made a heatmap over when the server is active.
To have it viewable online without using images, I decided to use plotly, which works great, but but can be a bit annoying to use.
Write a post about it :) !
Great article!
I'd imagine well-written, popular articles have long tails -- they continue accumulating likes months or years after they are written.
It seems like the time of day matters less for the long-tail likes, as opposed to the initial burst of likes... although maybe time of day could be a catalyst (if the right people are skimming the recent articles and sharing with their social media for example, then time of day could be the difference between a long tail and no tail).
I always strive to publish my blog posts on Mondays. Spoiler alert: It's mainly for those lucrative week streak badges so it gives me more headroom to delay the article by a day or two if something goes wrong.
By contrast, I don't know if this site treads Sunday as the end of the week.
data is beautiful.
In my personal experience, posting Thursday morning - evening US time gets most traffic to your posts and increases the chance of making into 'weekly top posts'.
Two reasons mostly
IMO I personally believe timing matters as much as the quality of the post to make it to 'top posts of the week'.
I agree that quality is the much important factor here, especially considering the amount of manual curation there is on the platform.
However, it is nice to see that likes are spread evenly during the week even though there are much more articles posted during the timeframe you mentioned.
This is all sorts of nice. Thank you.
I had just been wondering about this!
Great work! I had been wondering how I'd go about determining the best posting times. XD And now I know about Pandas; which is going in my hip pocket for future tasks. Thank you for sharing!
Thank you very much, I'm glad you liked it!
By any chance, do you know how to get articles of a specific user using the API?
Nice write up. Thanks! Could you share the data set?
Thank you Dmitry, I will asap, I'm just still adding some feature on it.
Amazing stuff you have shared here Pierre. Love this post. It will be really useful for a tech storyteller like me:)
Thank you!
Best time: when I have time