u/gloussou

I’m running a small side project where people anonymously report their current mood from 1–10.

I was looking at Europe using a rolling average of 100 responses, and noticed a very sharp jump at the end of April.

At first, it looked like European mood had suddenly increased by about 1 point.

But when I looked at the composition of those rolling windows, I found the problem: at one point, Spain accounted for up to 83% of the 100 European responses.

So what looked like a European trend was largely a change in the geographic composition of my sample.

Before correction:
The late-April rolling mean jumps by about +1.09 points.

I then tried a simple reweighting approach: Spain is still included, but it can contribute at most 20% of the weight in each rolling window of 100 European responses.

After correction:
The jump is reduced to about +0.89 points.

So Spain explains part of the apparent jump - but interestingly, not all of it.

This made me realize that fixing the rolling sample size doesn't necessarily fix the sample composition.

The data are anonymous and self-selected, so I'm not treating this as a representative estimate of European mood. I'm mostly interested in how best to visualize and analyze this kind of uneven crowdsourced dataset.

How would you handle this?
Cap each country's contribution? Weight countries equally? Use population weights? Or avoid producing a single “Europe” curve altogether?

Denmark -->7.38
Switzerland --> 7.14
Belgium --> 6.78
France --> 6.67
USA --> 5.62
Poland --> 5.40
Finland --> 5.31
Russia --> 5.01
Turkey --> 4.96

The biggest surprise for me: Denmark vs Finland — more than 2 points apart.

Obviously this isn’t a representative survey — people self-select into the site — but I find it fascinating to watch patterns emerge as the dataset grows.

Would you find this kind of live “world mood” dataset interesting, or is the self-selection bias too strong to make the comparisons useful?

Mood2Know.com

5,737 anonymous mood reports from around the world, rated from 1 to 10.

The line shows a rolling average of 100 consecutive responses, with ± SEM shown by the shaded area.

Some periods are remarkably stable, while others show sudden swings.

Can you recognize any major world events just from the dates?

Here were Popcorn, Raisin, Tofu, and Toast playing a fun Halloween themed basketball game! A bit early for this sort of thing this year, but its still one of my favorite examples of a 2 on 2 rat game and I wanted to re-share it!

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Un jour, en buvant des bières avec des potes, on galérait à trouver un coin pour soulager nos vessies ! Ajoutez à ça la possibilité de consigner ses pêches dans un journal… et l'idée était née.

Alors oui, je sais qu'il existe déjà moult applications pour trouver des toilettes, ainsi que des apps comme Poop Map ou Poopie. Mais j'ai quand même eu envie d'aller au bout de l'idée, notamment pour challenger un peu le côté design UI/UX : comment combiner une application sérieuse et grand public pour trouver des toilettes avec un réseau social un peu goofy ?

Ça a donné Pose Ta Pêche !

Le challenge de trouver des méthodes pour obtenir et classer des données fiables via des sources externes, puis de faire en sorte qu'elles soient vérifiées et enrichies par les utilisateurs selon leur réputation (karma), points d'action qui se régénèrent, badges pixel art tel la grande époque de Habbo Hotel, etc. était vachement intéressant.

Je ne saurai probablement jamais vraiment si les solutions mises en œuvre fonctionnent à grande échelle, car je doute que ce projet sorte un jour de mon cercle d'amis. Mais j'avais quand même envie de le partager avec vous !

Version web : https://posetapeche.fr