Now, we stretch out words naturally when we speak.
But stretched words (sometimes called elongated words) are fairly rare in book and other text corpora, and they aren’t represented well in dictionaries (if at all).
So we thought, let’s science this.
Stretchfulness in written text arrived in an abundant, accessible source with Twitter (along with the possible end of civilization but that issue is beyond the scope of our current project).
Dataset: 10% of all (140 character) tweets from September 2008 to the end of 2016.
We crafted* a series of regex-based tweet-sifters for capturing words that are naturally stretched in the wilds of Twitter.
We ended up with a skosh over 5000 “kernels” for stretchable words:
[g][o][a][l]: Any stretched version of goal with ordering of letters strictly preserved.
(ha): All words with h’s and a’s repeated in any order, as long as they start with an h and contain at least one a.
sq[u][e]: No stretch for s and q.
For each kernel, we plotted their frequency distributions.
Here’s [g][o][a][l]’s distribution.
The base word goal is used much more frequently then its stretched versions. But users tend to give goal a good stretch once they get going. They’re excited. Because football.
Now, we’re not saying that the tails of these distributions obey a power-law decay ... but we’re not not saying that either.
For those inclined, please feel free to fight amongst yourselves. You know who you are.
Here’s (ha).
The two-cycle jumps suggest that users try to keep on track with hahahaha but sometimes tragically suffer mistypings (hahahha; perhaps incredibly, we have more on this below).
We measure the “stretch” of a word with a standard Gini coefficient.
[g][o][a][l] is somewhat stretchy (G=0.108)
(ha) is stretchier (G=0.245)
A completely non-stretchable word would have G=0 as all instances of the word are the same. No one is special.
Next, we wanted to figure out the balance of a word’s stretch.
For [g][o][a][l], the g (a plosive) is rarely stretched much, while o gets the most stretch, closely followed by o and l.
We base our measure of balance on Shannon’s entropy H.
(ha) is balanced almost perfectly (we ignore letter order for these internally jumblable words (jumblable is fun to say)).
We see this kind of consistency of balance across stretch lengths for our entire collection of kernels.
Here are the most and least balanced stretchable words.
Not all words are stretched to reflect a vocalized form. Some are stretched for an attempt at visual emphasis, like capital letters also performs (but that’s yelling and rude).
And here are the most and least stretchy words (in our set of 5000+ stretchables; many words are not Elaine-level stretchworthy):
The stretchiness and balance of stretchable words we found in the Twitter wild make for solid parameters, filling out both dimensions well. Feels like science.
Last, we investigated how stretchables like (ha) go wrong with “spelling trees”.
Starting at the top with h, (ha) words trace down through the tree’s branches (h to the left, a to the right). Branch thickness indicates numbers of words.
P. S. Dodds, J. R. Minot, M. V. Arnold, T. Alshaabi, J. L. Adams, A. J. Reagan, and C. M. Danforth
Some questions to ask yourself and others:
What happened in the world over the last two weeks?
What about this time last year? Two years ago?
And what order did the major events happen in?
For Trump’s presidency, how easily could individuals recall and sort these example stories?:
- North Korea
- Charlottesville
- kneeling in the National Football League
- Confederate statues
- family separation
- Stormy Daniels
- Space Force
- the possible purchase of Greenland
With storywrangler, we’re hoping to enable or enhance the computational study of any large-scale temporal phenomena where people matter including:
culture,
politics,
economics,
linguistics,
public health,
conflict,
climate change,
and
data journalism.
J. R. Minot, M. V. Arnold, T. Alshaabi, C. M. Danforth, P. S. Dodds
We explore the dynamics of how Twitter users have responded to tweets made by Obama and Trump from their main accounts, @BarackObama and @realDonaldTrump.
For each tweet, we track three main characteristics as they evolve over time:
- Number of Favorites
- Number of Retweets
- Number of Replies (hard to measure—see our paper)
1. We curate and share usage time series of 1,000 1-grams that have mattered in March of 2020 (words, emojis, hashtags, etc.) for 24 languages.
We hope other researchers can use these time series to connect with other data streams.
2. We show that after a peak in January 2020 in response to the news from Wuhan of a novel contagious disase, the world’s collective attention dropped through much of February before resurging.