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Recent projects

Three recent projects which I have so far neglected to post about:

LATE EDIT: Andy Doro is exhibiting the Networked Byte Organ, which we worked on together, at Taller Boricua.

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Facebook Twitter
Verbs
get
set
ban
unban
create
expire
promote
revoke
run
add
remove
register
cancel
edit
invite
rsvp
delete
refresh
query
is/are
upload
send
mark
publish

Nouns
allocation
property
metric
restriction
user
info
token
session
authorization
permission
batch
comment
friend
count
cookie
member
tag
url
handle
group
translation
link
string
message
thread
folder
note
notification
list
e-mail
page
fan
photo
album
profile
sms
stream
option
like
filter
status
video
limit

Verbs
get
show
update
destroy
post
put
exists
verify
end
follow
leave
report
request
authorize
authenticate

Nouns
search
trend
status
timeline
mention
retweet
friend
follower
direct message
friendship
id
account
session
delivery device
color
image
profile
favorite
notification
block
spam
search
token
test

I made this list by combing through both services’ API documentation and extracting noun-like words and verb-like words from the names of resources or methods. Facebook count: 24 verbs, 43 nouns. Twitter count: 15 verbs, 24 nouns.

Here’s what I think these numbers mean.

Facebook manages many different kinds of content, and allows you to perform many different kinds of actions on that content, though the actions that you can perform on one kind of content are inconsistent with the actions you can perform on another. Twitter has fewer types of content, and a more consistent set of HTTP-like actions to perform on that content.

The Facebook API tends toward the baroque and insular, while the Twitter API tries its best to be a part of the web. In general, the Twitter API is much more straightforward.

I think this simplicity—this paucity of nouns and verbs—has been an important factor in Twitter’s widespread growth among both users and developers.

Developers can be confident that—even if Twitter’s API changes—they’ll still be doing mostly the same actions (getting, posting, updating) on mostly the same things (statuses, friendships, direct messages). Users know exactly how all the moving parts of Twitter work together, and are therefore better able to understand how a given application might augment that.

On Facebook, the opposite is true. User statuses, notifications, event invitations, feed stories, photos—they all have different interfaces and behave in different ways. Even long-time developers can’t keep track of how everything works together.

Interestingly, Facebook’s platform roadmap says that one of Facebook’s goals is to “focus [...] communication on the stream and Inbox,” which will make communication on Facebook feel much more like Twitter. I think this is a smart move—anything that simplifies the user’s model of how communication on Facebook works is a good idea, both for users and developers.

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longestpoem

The procedure: scrape Twitter’s public timeline and find rhyming couplets. The effect is striking: even though the juxtaposition of tweets is essentially random, the presence of rhyme gives them a strange cohesion.

The only obvious weirdness comes when the procedure tries to rhyme emoticons, as in the following couplet:

Finished a paper for class and time to relax. Yay me!!
Showered and feeling good :) How are y’all smelling? :b

On second thought, I like that: it’s as though the procedure suggests you read the emoticon aloud in a cutesy way (“time to relax, yay me! / … how are y’all smelling? colon lowercase bee”).

(via)

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Murmur Study

Murmur Study from Christopher Baker on Vimeo.

Murmur Study is a physicalized reading of Twitter, created by artists Christopher Baker and Márton András Juhász. (More details and photos.) The process, according to the artist’s site:

This installation consists of 30 thermal printers that continuously monitor Twitter for new messages containing variations on common emotional utterances. Messages containing hundreds of variations on words such as argh, meh, grrrr, oooo, ewww, and hmph, are printed as an endless waterfall of text accumulating in tangled piles below.

I’m not quite sure I understand the reason to single out tweets that match those particular text patterns—how is this format more appropriate for illustrating the mass of “emotional” tweets? (as opposed to tweets about kittens, tweet spam, tweets from political figures, tweets about thermal printers, etc.) Stunning in its scale, nevertheless.

See also: bitfall and Simanowski’s keynote from e-poetry 2009 (not linked, because I can’t find a link).

(via rhizome.org)

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Hulu – The Tonight Show with Conan O’Brien: Shatner Reads Palin’s Tweets.

Here we have an example of politician talk satirically repurposed as poetry. The practice itself is nothing new (see Donald Rumsfeld), but Shatner’s performance here is a cut above.

These tweets form the text as performed: “From sealife…”, “Tourists from across…”, “Awesome Alaska night…”, “Left Unalakleet warmth…”. It’s interesting that Conan claims the text to be “verbatim,” even though it’s a collage of many non-contiguous tweets.

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5-in-5 Day 2: twbasic

My day 2 project is called twbasic. It’s an implementation of classic, line-numbered, home computer-era BASIC that runs on top of Twitter. You can start using twbasic right now by twittering your program listing to @twbasic. Here’s how to get twenty rolls of a six-sided die:

@twbasic 10 for i = 1 to val(ARG$)
@twbasic 20 print rnd(6) + 1
@twbasic 30 next i
@twbasic run 20



(The above screenshot depicts twbasic providing a listing of the program above, and then the results of running the code.)

A reference for using twbasic can be found after the jump.

Read the rest of this entry »

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