I made this over the last two days in Node.js. The analysis is still pretty simple, and I'd like to expand it over time. It updates every 30 minutes, and you can already see that there's some significant shifts in literacy between the daytime and very early morning.
I had originally started the project to grab Flesch-Kincaid grade levels, but it was taking me 45 minutes to analyze about 20 pages of comments, so I switched it out for Automated Readability Index.
I really wanted to include HN, as I feel it's in a period of social shift, but it wouldn't have the same sort of statistical significance due to its size (it would most likely just be meaningless jarring jagged lines across the graphs). If someone can think of a good way to include HN, I'm all ears. I'd also be interested in other methods of writing analysis that I could include.
Thanks. It compares misspellings to correct spelling counterparts (anyways:anyway), so that, in itself, should account for sample size changes in Twitter. The Reddit sample size (should) stay constant, as I'm grabbing /all/comments every 15 seconds, which from everything I've seen is about 3 times longer than it takes for it to turn over.
I have been thinking about subreddits and hashtags, but I'm not sure what would be interesting to people and still have enough statistical significance to update every half-hour/hour. If I were going to release this on Reddit, I'd have probably arranged it as a comparison between all the major subreddits. As for hashtags, their impermanence makes them a difficult measure.
I don't have the source up yet, but it's pretty simple. I just get the json output of /all/comments and get the Twitter streams for the words I'm checking with ntwitter and then do the basic math to analyze it every half hour. I render the page myself with the backlog of values and then send it updated info as I process it, so if you leave the page open, it should keep fresh.
What exactly is Twitter sample size? If it means "the number of records processed", why does it vary over time?
One way to approach the "significance" problem for smaller communities like HN is to create larger bins of misspellings/correct spellings. However, I don't think you're going to see many "anyways" or "yea" on HN at all, much less significant fluctuations over time.
Finally, an interesting question might be to what extent individuals fluctuate in their ARI/FK levels in different contexts. What if a poster in /r/lolcats writes a very asinine comment but then goes five minutes later to /r/programming to speak intelligently. Or is it the case that an idiot is an idiot, regardless of context?
Sample size is the anyway|anyways|yeah|yea instances. I get those as they get posted, so that's why it fluctuates with time. Since they're so commonly used, it should incidentally give you an idea of all of Twitter's load. I'm also grabbing some other words that I haven't implemented on the clientside yet, but I don't include them in the sample size.
HN comments tend to be more varied and sparse, so I think you're right that measuring yea:yeah, etc. wouldn't be too enlightening. That said, I've noticed a defined qualitative shift in HN comments over the past few months, and I'd like to develop ways of measuring that before they reach Reddit/Twitter levels.
As for your last point, I could track individuals but a relational comparison based off of /all/ data would be pretty difficult due to the number of comments vs. the few number of any individual's comments. Also, ARI isn't a great metric (hence me putting it in a tiny graph) because it measures chars instead of syllables. For example, "FFFFFUUUUUUUUU" has the same score as "constructivism".
I like the general idea and presentation, but if you're seriously desiring to measure social shift, I think basing that judgment on "yea/yeah" and "anyway/anyways" usage is problematic. The former is dropping a silent letter from an arbitrarily-spelled informal word. The latter is a difference in dialect (i.e. culture), not intelligence.
Quibble: I don't think I'd consider either "yea" or "anyways" misspellings per se, at least in informal chatting. "Anyways" is heard in some spoken dialects, so you'd expect it in written forms of the same dialects. And I think of "yea" (in modern usage) as a variant of "yeah", like "yah" is. All of them are phonetic spellings of dialectal versions of "yes" to begin with (along with "yep" and "yup" and such).
They don't, at least, seem in the same category as clear spelling errors like copyright/copywrite, or bureaucracy/beaurocracy.
I chose those two to start things off for two reasons: The first is that they stand out to me as pretty basic misspellings that don't appear in any sort of popular literature, so it speaks to someone's reading experience. You can take that for whatever importance it is to you, personally. The second reason is their frequency. If the unit of measurement was larger than a half-hour or hour, I could measure a bunch of other things (which I might still do). As it stands, anyway|anyways|yea|yeah occur 25k-70k times per 30 minutes.
I guess I'm disagreeing that they're "basic misspellings" in the first place, or related to someone's reading experience. Informal spoken English is often different from written English, and some people Tweet closer to how they'd speak. That doesn't mean that the same people would write the same way if they were writing a book (they probably wouldn't use "yeah" in a book, either), or that a certain dialect of spoken English is "incorrect".
I think you're measuring something other than spelling here, closer to measuring prevalence of certain dialects. Especially in the "anyways" example: if there's something objectionable about that word, it's a usage objection, not a spelling objection. People really do say "anyways", and that would be the correct way of writing it if you accept that usage. Spelling it "enyways" or "annyways" or something would be an orthographic error, on the other hand.
There's no link between spoken and written English in the context of yea:yeah, as they're pronounced the same but either written correctly or incorrectly.
As for anyways, I think your classification there will depend on who you speak to (which is the point of the analysis).
The problem with your conclusion that 'anyways' is a misspelling and you chose it because it is popular (high 'frequency') is that it displays a common misunderstanding purists have of a language's evolution.
As Jorge Luis Borges once said, all words were once neologisms.
For something to be standardized or make it into a dictionary, lexicographers look at its popularity. If we reduce language to the most simplest idea, it is exactly this: a set of popular phonetic sounds from which humans derive agreed similar meaning.
The phenomenon of adding an extra sound to the end of a word is called paragoge.
For anyone that is interested and is not familiar with it, linguistics has a lot to say on the addition and subtraction of sounds at the beginning, middle, and end of words. Here is a link: http://en.wikipedia.org/wiki/Metaplasm
Great points. I went into this knowing full-well that to many, many people, these misspellings were not considered as such. As the graph suggests, though, the majority of people still use the "older" spellings of "yeah" and "anyway". There are two ways of spelling each one (with "yea" and "anyways" considered to be the more nascent), and I'm approaching this from the standpoint of why the spellings are changing. Is the language evolving (suggesting improvement) or devolving (suggesting perpetuated errors). We could discuss linguistics, but what indicators are there from the data showing Reddit has a lower percentage of yeas and anywayss compared to Twitter? Is it simply the medium? How does that data compare to your anecdotal classification of the respective aggregate intelligence level of both sites' users? What do you think the data would say about HN vs. Youtube on the frequency of these (as you would consider) both valid spellings? This all boils down to me labeling the graph as common misspellings, but ignoring that, you should be able to make of the graphs what you will, as there's no associated hypothesis or conclusion.
That's why I'm not relying on any single metric, as well as framing it in the context of time. If Justin Beiber asks his fans to vote yea or nay on something, it might mess up the yea:yeah metric, but it should eventually normalize, and while it isn't normalized, the anyways:anyway metric should still be fine (Unless JBeibs is asking his fans to vote yea or nay on naming his new album "Anyways").
Those might work for Twitter, but there may be some lurking variables on Reddit. While they never bother to catch yea and anyways, Redditers do have a tendency to pile on about the few grammatical/spelling errors they can spot. For example, if a post title says "could of", there's likely going to be multiple comments discussing the error and thus tripping my hooks.
Edit: As for the axes, I made some stylistic decisions that I wouldn't dare to make were this being submitted to the Annual New Media Socio-Linguistics Journal.
"Yea" pronounced "yay" is a valid word with practically the same meaning as "yeah" (affirmative). Granted "yea" is archaic and most of the instances you are catching are just yeahs with the "h" dropped, but it would be nice to choose something that is definitively wrong. The anyway/anyways is a better choice and I hope that "anyways" does not become widely accepted because it is like Bieber to my ears.
I'm working on the assumption of statistical significance of people using "yea" pronounced "yeah" vs. the very few people who might have reason to take to Twitter to use "yea" in a voting or biblical context.
19 comments
[ 2.9 ms ] story [ 60.7 ms ] threadI had originally started the project to grab Flesch-Kincaid grade levels, but it was taking me 45 minutes to analyze about 20 pages of comments, so I switched it out for Automated Readability Index.
I really wanted to include HN, as I feel it's in a period of social shift, but it wouldn't have the same sort of statistical significance due to its size (it would most likely just be meaningless jarring jagged lines across the graphs). If someone can think of a good way to include HN, I'm all ears. I'd also be interested in other methods of writing analysis that I could include.
It might be even more interesting to do comparisons between subreddits and comparisons between hashtags or followers of certain figures.
Also do you have a link to the source?
Keep up the good stuff!
I have been thinking about subreddits and hashtags, but I'm not sure what would be interesting to people and still have enough statistical significance to update every half-hour/hour. If I were going to release this on Reddit, I'd have probably arranged it as a comparison between all the major subreddits. As for hashtags, their impermanence makes them a difficult measure.
I don't have the source up yet, but it's pretty simple. I just get the json output of /all/comments and get the Twitter streams for the words I'm checking with ntwitter and then do the basic math to analyze it every half hour. I render the page myself with the backlog of values and then send it updated info as I process it, so if you leave the page open, it should keep fresh.
One way to approach the "significance" problem for smaller communities like HN is to create larger bins of misspellings/correct spellings. However, I don't think you're going to see many "anyways" or "yea" on HN at all, much less significant fluctuations over time.
Finally, an interesting question might be to what extent individuals fluctuate in their ARI/FK levels in different contexts. What if a poster in /r/lolcats writes a very asinine comment but then goes five minutes later to /r/programming to speak intelligently. Or is it the case that an idiot is an idiot, regardless of context?
HN comments tend to be more varied and sparse, so I think you're right that measuring yea:yeah, etc. wouldn't be too enlightening. That said, I've noticed a defined qualitative shift in HN comments over the past few months, and I'd like to develop ways of measuring that before they reach Reddit/Twitter levels.
As for your last point, I could track individuals but a relational comparison based off of /all/ data would be pretty difficult due to the number of comments vs. the few number of any individual's comments. Also, ARI isn't a great metric (hence me putting it in a tiny graph) because it measures chars instead of syllables. For example, "FFFFFUUUUUUUUU" has the same score as "constructivism".
They don't, at least, seem in the same category as clear spelling errors like copyright/copywrite, or bureaucracy/beaurocracy.
I think you're measuring something other than spelling here, closer to measuring prevalence of certain dialects. Especially in the "anyways" example: if there's something objectionable about that word, it's a usage objection, not a spelling objection. People really do say "anyways", and that would be the correct way of writing it if you accept that usage. Spelling it "enyways" or "annyways" or something would be an orthographic error, on the other hand.
As for anyways, I think your classification there will depend on who you speak to (which is the point of the analysis).
As Jorge Luis Borges once said, all words were once neologisms.
For something to be standardized or make it into a dictionary, lexicographers look at its popularity. If we reduce language to the most simplest idea, it is exactly this: a set of popular phonetic sounds from which humans derive agreed similar meaning.
The phenomenon of adding an extra sound to the end of a word is called paragoge.
For anyone that is interested and is not familiar with it, linguistics has a lot to say on the addition and subtraction of sounds at the beginning, middle, and end of words. Here is a link: http://en.wikipedia.org/wiki/Metaplasm
Admittedly that doesn't apply here with "anyways" but it perhaps does with "yea". Perhaps throw out tweets over a certain length?
Expanding on this could lead to some interesting insights. How about "could of" and "could have"? and the old "could care less"...
Edit: As for the axes, I made some stylistic decisions that I wouldn't dare to make were this being submitted to the Annual New Media Socio-Linguistics Journal.