

Show HN: Bubbles – A minimalist visualization of what's new on Twitter - amtmz
http://bubbles.amizrahi.com/

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gravity13
Hm. I hope this comes across as constructive criticism, but I'm not sure the
visualization type matches well with the data you are presenting.

The movement of the bubbles doesn't seem to convey any useful information,
it's just added flare. I'd get rid of it. (you might enjoy some classic books
by Edward Tufte too)

The placement of the bubbles seems to be completely random. You can use your
space more wisely.

You are trying to represent growing trends with this visualization, perhaps
some sort of component should go into representing that. Maybe you can
represent that in your x and y plane somehow?

You're viz looks blurry on retina screens. You can fix this by setting the
width attribute of your canvas element to twice what you want it to be, and
adding a css style width at what you want the width to actually be.

Edit: I just read the text describing what velocity actually does, nvm, I'm
dumb.

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amtmz
Thanks for the criticism! This was my first time doing data visualization, so
any feedback is really helpful. I'll definitely check out some books by
Professor Tufte.

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baronofcheese
A friend and I did something related to this in a project, where we attempted
to analyze a hash tag, by fetching the latest 100 tweets about that hash tag.
It is very useful in trending topics. For instance searching for "Stuart"
right now reveals a lot of other relevant tags and attempts to find out what
the general mood is of the words used in the tweets doing a simple Sentiment
Analysis using AFINN. Try it here:
[http://hashtagram.dk/?t=stuart#hashtags](http://hashtagram.dk/?t=stuart#hashtags)
(might get unavailable due to many visits, and don't mind the broken Instagram
images at the bottom, site needs updating due to API changes). If for some
reason the site does not work, see a static version here:
[http://imgur.com/EQzMrUP](http://imgur.com/EQzMrUP)

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tabrischen
It might be interesting to do a separate one just on Instagram. I have no idea
what half of the trending hashtags on Instagram means and a picture speaks a
thousand words.

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baronofcheese
The Instagram pictures were there to back up the understanding of a hashtag
generally. For under the world cup, it was really easy to find out what
#worldcup2014 was about. Interestingly refreshing the page for a specific page
during some kind of event, such as the world cup one, or during 24 Hours of Le
Mans, it is easy to follow what people say about it. In the Le Mans case it
was also quite easy to follow that for instanced someone crashed their as the
sentiment drastically went to the negative side, and you could back that up
with pictures of the crash on Instagram.

But I agree, a separate one analyzing the pictures could be valuable as well.
However, Instagram pictures often don't contain much text apart from a wall of
hash tags. So the text might not be super important to analyze. Anyways,
picture analysis might be slightly more difficult and more resource intensive
than text analysis, but very interesting as well.

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qpleple
You could run a topic model [1] to display, say, the top 20 topics discussed
on Twitter. LDA [2] is a good one.

[1]
[http://en.wikipedia.org/wiki/Topic_model](http://en.wikipedia.org/wiki/Topic_model)

[2] [https://pypi.python.org/pypi/lda](https://pypi.python.org/pypi/lda)

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hnriot
It's more complicated than that, an LDA clusters documents into topics but
it's non-trivial to determine what the topic is. You can use the head words of
a tf.idf analysis but those still don't necessarily equate to topics. For what
you're looking for I think you'd need ontology tagging so a bunch of tweets
mentioning soccer players would give a topic word like 'soccer'. The problem
then becomes the granularity to ascribe topic to, for example, should it be
soccer or sports? Should it be more specific still. Then there's the non-
obvious things like a plane goes down and the topic would likely be aviation,
but that hardly gives any new information. Representing 20 topics on twitter
is very difficult problem. Someone dies and the topic of "death" shows up, not
very useful. I'm not disagreeing with you but rather saying that what you're
suggesting is a very difficult problem to do in any useful and meaningful way.

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whistlerbrk
This is great and I bet there are a ton of directions the author can go from
here, pausing bubble movement on hover, clicking on bubbles to see the tweets,
etc. JSYK, I reloaded and a fast moving but small bubble became a much slower
moving much bigger bubble.

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cultavix
Looks really cool. Perhaps filtering out certain words would make it better?
Most of the words don't really tell me what's going on. But I guess I could
just look at the trending hashtags.

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bsg75
Color (as in a heatmap) would be a better indicator of rate than the velocity
of the bubbles. Currently one is zipping around so fast I cannot read it.

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andrewliebchen
Velocity can make it a little hard to track changing words. Intensity of
bubble color might be a good parameter to change for this metric.

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iambot
should probably remove stop words

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scrozier
and common words that aren't proper nouns

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tabrischen
especially numbers that are contextless. right now one of the biggest bubbles
are 1 and 2.

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amtmz
I'm adding a lot of common words to the blacklist right now. Expect to see an
update shortly!

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moinnadeem
Is this open source? I'd love to take a look around.

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amtmz
Yep! Feel free to star / look around on GitHub.
[http://github.com/amizra/bubbles](http://github.com/amizra/bubbles)

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hackhackpad
can you filter noun? most of the world is not really informative keyword, and
having them as bubbles only add noise.

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akhatri_aus
Uses /GET every 60 seconds.

[1] setInterval(grabWords, 60000);

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mytochar
Is this bad? It's a site to see what's currently trending on Twitter. I
figured it'd update more often, actually

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infinitebattery
this is awesome. is this real-time?

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jat850
I'm guessing it must be close. One of the bubbles is "Stuart", and Stuart
Scott of ESPN is trending on Twitter quite heavily today after passing of
cancer.

