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"Backchannel" communication mechanisms like Twitter are going to revolutionize everything from meetings and classrooms, to our day-to-day relationships… but first we have to learn to deal with them appropriately. At SXSW, the Twitter backchannel turned an audience into a mob. The session leaders, not having access to the backchannel, had no idea what was happening, or why the mood of the audience had changed so rapidly.

This is an article about the keynote interview at the 2008 SXSW Interactive Festival. If you haven't heard the details, you can do a Google search on "SXSW Lacy Zuckerberg interview", or read one of many articles such as Wired’s SXSW: 2008, the Year the Audience Keynoted.

First things first.

I am not going to make any comments here about whether it was a good interview, bad interview, inappropriate topic, inappropriate people… that's not the point of this article, nor do I think it was really the reason why the audience at SXSW got so upset. Please keep any comments on topic, and away from the details of the interview. In particular, keep in mind that this was not the only session at SXSW that ended up with an audience verbally attacking the panelists and taking over the session.

At the SXSW Keynote interview, an audience of conference-goers acted unusually; they became a mob. I mean that in the sense (as Dave Winer does in his "Twitter is not a chatroom" podcast) of a crowd out of control—a crowd doing things that individuals would not. This is the kind of behavior generally seen at political events; or meetings where the audience is emotionally tied to the subject matter. An interview or panel that seems off-topic usually gets a few walk-outs and a bad rating in the "What can we do better next year?" survey; not an audience revolt. I find that very interesting, and I have some ideas as to why it may have occurred.

“Where are the yellow comment cards?” - 51 minutes

In the nineteen-sixties, psychologist Stanley Milgram ran a series of experiments which are probably familiar to anyone who has taken Psychology 101. He asked subjects to inflict pain on a person when that person made a mistake on a test. A number of interesting results came out of the experiment, but one variation in particular is relevant here. The willingness to administer shocks increased when the recipient was more remote. It's hard to de-humanize someone who is right next to you. On the other hand, if you can't see them, and don't know them, your emotional attachment is greatly reduced, and you will do (and say) things you would never do in person. This isn't news to anyone who has been involved in an email flame-fest, or posted something online that later got quoted face-to-face. We say things online that we would never say in real life.

“sarah lacey is in love with zuckerberg, me thinks”- 11 minutes
“someone offering me $20 to yell "Beacon Sucks". paying for my night of drinking if I do. I definitely should.”- 17 minutes
“Someone just yelled out, "BEACON SUCKS!" - yes, that's how this is going...u should be here :)”- 18 minutes
“Holy cow, whoever this is interviewing Mark is horrible! I think she is in love with him. Hold on...she might go in for the kiss!" - 20 minutes

Mobs form when individuals feel anonymous, and believe that their feelings and behaviors are shared by others. When the behavior becomes visible, and when nobody reacts negatively to it, the behavior gets amplified, with more and more people joining in. But why would this happen in a technical conference on a relatively unexciting keynote topic?

The blame, of course, has fallen on Twitter, because it was through Twitter messages that the revolt first began to appear. More and more, services like Twitter are being used as "backchannel" communications mechanisms by which the audience can provide non-interruptive feedback to each other, and to the people running the event. In this case, the people running the keynote obviously weren't tracking the backchannel (perhaps "sidechannel" would be more accurate in that instance). Rationally, there's no reason to get frustrated that your Twitter comments are being "ignored" by someone whom you know isn't reading them, but frustration probably did play a role here as well.

One thing that wasn't clear to me in the discussions about the keynote, was whether the Twitter phenomenon was "real", or whether it was just the result of a lot of posts by a few (prolific) individuals. So I decided to see if I could gather up a collection of all the Twitter messages sent from the keynote, during the keynote.

There's a detailed methodology section at the end, but this is the quick summary.

1. Searched for all users who mentioned "SXSW" between Thursday and Sunday the week of the conference.
2. Searched for all users who mentioned "facebook", "lacy", "zuck" or "keynote" within a few hours of the event.
3. Manually narrowed down the resulting 4000 messages.

The final result was nearly 2000 Twitter messages, sent by 500 users. I know that some of the messages I included were irrelevant, and I'm sure I missed others. But overall, that comes to an average of close to one message every two seconds! There is no question that backchannel communications were very active during the keynote.

It is my belief that what happened was a combination of standard mob behavior with the side-effects of the behavior observed by Milgram. People sent messages, using the usual level of rudeness that occurs in electronic communication. Those messages were read by people also at the keynote. As the level of vitriol rose in the twittersphere, it also fed back into the people in the crowd. "Surrounded" by other people feeling the same way, the animosity and feeling of anonymity moved from the virtual world into the real world; and a virtual mob turned into a real one.

Is that what really happened? It's impossible to say for sure. Twitter is not a real-time protocol, and people take time to type messages, so it was impossible to directly connect a particular message to a particular time during the talk. Also, there is no way to tell who in the audience saw the messages (or heard about them from a seat-mate who did).

What I have done, is gather up the Twitter messages from the keynote, and overlay them directly on a video of the interview. I used the video posted on Viddler by allfacebook. It cuts off somewhere near the end, but was the most complete version I could find.

The following video is annotated with all the Twitter messages I collected. In some cases you'll have to pause to read them, since they go by too quickly. I grouped together any posts that occurred within one second of each other to make it a little easier to scan. Since I saw apparent lags of up to fourteen minutes between what some tweets described and what happened, I finally settled on a five minute lag for all tweets. So keep in mind that the tweets do not directly correspond to the current scene in the video, and two adjacent tweets were not necessarily written, sent, or recorded at the same time.

Because this version of the video is hosted at Viddler, you can not only comment on my blog, you can also make comments in the video, attaching them to particular frames. Please feel free to do so.

The Annotated Video

Note: The annotations will be easier to read if you expand the video to full size (click in the upper right hand corner of the video).

The Annotated Data

The following charts show the relationships between messages ("tweets"), users, content and time. I have actually done a minimal amount of analysis. Most of my time was spent filtering the data and annotating the video. If someone wants to examine things some more, I'd be happy to turn over the data and my (very) motley collection of Perl scripts.

The charts are all plotted against time, usually expressed as HH:MM and sometimes just MM. All values are the sum of the activity over a minute, or in some cases, five minutes.

Number of Messages Sent and Number of Users

The blue area shows the total number of messages being sent each minute. The red area shows the total number of new users (i.e. those who haven't previously sent a message). I actually have not spent much time examining what happens in the video vs. what happens in the tweets. Does something interesting happen around 45 minutes to cause a spike, or is that a conincidence?

The final total was 512 users and 1857 messages.

First and Last Sending Times (With Count) Per User

The following two charts (attempt to) provide a view of how often and how long people were posting messages. I could have done a standard mean/median/mode chart, but I wanted to include the start and stop times. In both charts the vertical axis represents the time at which someone sent their first message, and the horizontal axis indicates when they sent their last message. (So the diagonal line represents people who only sent one message.) The first chart uses the size of the bubble to indicate how many messages the person sent. The second chart uses the color of the square.

The most messages sent was 65 (by three people). A quick check indicated that the frequent posts seemed to be from people providing a running report on what was happening.

Frequency of Top 50 Words over Time

You'll probably want to click on the image and view the PDF version.

This chart shows how often each word appeared during a given five minute period. The most common words appear on the right. For instance, we can see that the words "audience", "people" and "crowd" grow in popularity over time; probably indicating that people were tweeting about the behavior of the audience as the interview progressed.

Frequency of Top 50 Words by User over Time

This chart addresses the question of whether the messages were being sent by just a few people, or many. Here the value for each five minute period is the number of people who used a word. Where the previous chart counted all instances of a word, even if used multiple times by a single user; this counts only one use per user. The chart appears very similar to the previous one, indicating that the use of the top fifty words was quite wide-spread.

Top 50 Words, Total Frequency, and by User

Like the previous chart, but showing just the totals. The red bars indicate the number of people who used a given word. The blue bars indicate the total number of times the word was used. Again, the usage appear to be fairly evenly distributed.

Conclusion

The Twitter transcript makes it clear that there was an early and constant stream of negative comments flowing from a large number of senders. The lack of accurate timing information makes it impossible to tell for certain whether that was something that started small and spread, or exactly how it erupted into real life. However, it is clear that the conversations in Twitter did lead to the same level of real life behavior and dialog. While it could just be attributed to the general decline of societal mores, I believe my original assertion as to the connection and influence of the virtual and real worlds is potentially valid. It would be interesting to see a more detailed and rigorous study of future events. (Sounds like a good Sociology/Psychology/Anthropology thesis for someone. :-)

Twitter provides a communication channel which augments, rather than interrupts, existing communications. As such, it makes it possible for people to communicate both within a group, and (in structured events) to the leaders of a group, all without disrupting the normal progress of the activity. If that sounds like too much for a panel discussion or interviewer to manage, consider that most reporters in traditional media (not to mention football quarterbacks) have similar mechanisms for receiving information while they work. Whether increased multitasking is a good thing from a quality standpoint is a different issue. For that, look at the research that Howard Rheingold has been doing in the areas of multitasking and backchannels.

The Twitter backchannel can definitely have a positive influence. As a remote observer of SXSW I was not only able to receive ongoing summaries of sessions, but I could suggest questions for attendees to ask, and provide resources to panelists while they were in an active session. The backchannel can provide a low-key mechanism for alerting presenters to issues, offering support, and of course organizing and coordinating group actions. The issue, is how to keep group actions from growing out of control. I believe that requires education (or perhaps just a new generation of users) about the differences between virtual and real communication, and the dangers of transporting emotions directly from one to the other. I believe it also requires responsibility on the part of backchannel spectators.

There is a tendency in online discussions to let flames burn themselves out. After all, it's the virtual world, not the real one. "Getting involved" can be a pain. But as the SXSW events show, the boundaries between real and virtual get thinner every year, and virtual emotions can cause real-world harm. I greatly admire Sarah Lacy's ability to deal with the abuse she has gotten and move on. A reporter has to have a tough skin, but it still can't have been easy. She didn't deserve the abuse that was dished out on Twitter, let alone what happened in the auditorium.

As citizens of the online world, we have a responsibility to step forward when we see people misbehaving. It doesn't take much to tone things down. People need to be reminded that the target of their frustrations is a real person. They also need to be reminded that their persona, though virtual, has its own reputation to think about. The members of an online mob are in fact far less anonymous than those in a real mob. I was rather shocked when I happened to notice that one of the tweets I quoted above was actually made by someone I follow on Twitter. It was more sophomoric than mean, but it still contributed to the overall mood. Finding out who said what during the conference is a simple task for anyone with access to Google. We need to live our online lives under the assumption that everything we say, and everything we do, no matter how private it seems, is going to contribute to our overall reputation. That's a good thing, but it takes getting used to.

“So it took an eight-year-old child to bring 'em to their senses.... That proves something - that a gang of wild animals can be stopped, simply because they're still human. Hmp, maybe we need a police force of children.” –– Jean Louise (Scout) Finch in "To Kill a Mockingbird"

Finally, when we misbehave online (as we all invariably do at one time or another) we need to own up and apologize. Fortunately, the same attributes that make it easier to screw-up online, also make it easier to apologize online. You don't have to do it face to face, a quick tweet or email message works just fine. If you were at the SXSW keynote, you might consider that.

Predictions

This experience, and the past month that I've spent intensively using Twitter, have led me to a few beliefs about where this all is leading us.

The first one easy. Anyone who runs a conference, panel or large meeting without monitoring the backchannel is simply asking for trouble. Ironically, SXSW did have an official chatroom for the keynote, but that did not receive as much traffic, nor was it being monitored as a backchannel should be.

The second one is longer term. For several generations social networking on the computer has been derided as not having the depth or value of real life social interactions. Tools like Twitter (and Facebook), which blur the lines between work and home, important and trivial, and which deliberately create a malleable and ambiguous set of simple tools ("status", "poke", "what are you doing") are the primitive forerunners of what the next generation will take for granted. The always-on aspects will surely migrate to phones and become a constant part of our online life. The interfaces may be crude, but I am already more connected to the lives of people halfway across the world than I am with my next door neighbors. That knowledge extends from the trivial (I'd love to have dinner at Adam Engst's house, he cooks a lot of interesting stuff) to the critical (Susan Reynolds' fight with breast cancer has led to a wonderful support group and a great funding effort). It isn't a matter of not spending time with the neighbors, it's that I don't have a real-time, ongoing conversation with my neighbors day in and day out! The next generation is going to look back at pre-computer-mediated social interaction and say that we are the ones who had no depth in our relationships.

Methodology

• Searched for all users who mentioned SXSW between Thursday and Sunday the week of the conference (4068). For this I used the Terraminds Twitter Search API with a filter for the correct dates.
• Searched for all users who mentioned "facebook" (256), "lacy" (80), "keynote" (290) or "zuck" (111) between 1pm and 4pm on the day of the interview.
• I then took the list of 4232 unique users from the previous searches, and gathered every message they posted between 2:05pm and 3:15pm. (I initially gathered more, but then narrowed it down to those times as my best guess for the start/end times of the interview, as well as the most likely lag time for tweets.) The Terraminds search doesn't do per-user searching, so I used Tweetscan. Tweetscan doesn't provide an API, so I screen scraped the results.
• Narrowed down the resulting 3562 messages by splitting them into two groups. The first group matched the previously searched keywords (2039), the second did not (1523). I scanned the first group for messages that didn't look like they belonged (wrong topic, different session). I scanned the second for things that I might have missed. Those were quick scans and sloppy. The end result was to remove 66 entries and add 30.
• I should note that there are several things I didn't do but could have. That includes searching for additional keywords, and also checking the communication chains. E.g. If user @a sent a message to @b, then I should check @b's messages as well. I also did not include the Meebo transcriptsin the results, although that would be easy to add.
• The final result was 512 users and 1857 messages. (One user was removed in post-processing, when I realized that "twitgeistr" was a bot that simply reported on keywords that it found in the public stream.)
• I then spent an inordinate amount of time figuring out how to subtitle the video, including attempts at two different sub-title formats. The final solution involved Final Cut Pro and programatically generated XML files that specified text-effect overlays with differing offsets depending on the number of lines. If anyone at Apple wants to write a bit more documentation on FCP XML files, and provide a few more examples, that would be just fine with me. The annotations have all @user references changed to "@", and all links replaced with "[LINK]". The data is public, but I see no reason to make it easy to embarrass individuals.
  
• The keyword processing is also done with a Perl program. Lingua::StopWords was used to remove common English words from the list. Lingua::Stem was used to stem the words (e.g. make "improve", "improves" and "improved" all map to the same word). Stemming exceptions were made to ensure that the words in the top fifty were all spelled correctly (stemming programs don't really care if the result is a real word, only that the mapping is correct. "improves" normally gets stemmed to "improv"). In addition, I added some other common mappings. "sarah", and "lacey" both map to "lacy". "mark", "zuck", "z" and "zuckerburg" all map to "zuckerberg". "fb" maps to "facebook", and so on. I also added some additional stop words. I tossed "just", "like", "now", "can", "go", "got" and a number of other words that were very common but which didn't really carry any emotional content.
• The same Perl program generated the XML files, as well as a set of tab-separated data files for processing by DeltaGraph. DeltaGraph is a very powerful charting software (particularly if you are dealing with data sets with missing data), but some better (dare I say, "prettier"?) defaults and an updated UI wouldn't hurt.

If anyone is feeling particularly masochistic, I would be happy to package the whole mess up and make it available for download. Let me know.

Postscript

All the data gathering and analysis here were done by myself and for that, and any of the errors that are inevitable in such a rushed project, I am solely responsible. However, I'd like to thank a few people who contributed, knowingly or unknowingly.

• Jeremiah Owyang, Robert Scoble, Marshall Kirkpatrick and Dave Winer all discussed in detail what was happening, and why, on Twitter and on their blogs.
• Howard Rheingold is doing some very interesting work on backchannels and multitasking. His tweets on the daily progress of his Virtual Communities/Social Media class are quite interesting and relevant to this discussion.
  
• At a recent Boston Tweetup someone from the SOURCEBoston group suggested that I do a word analysis; a suggestion which added several days to the project, but was definitely worth it. Unfortunately I don't remember who it was, let alone their name.
• Also at the Boston Tweetup, Dmitri Gunn reminded me of the name of the other SXSW session which had a Twitter dustup, "Social Marketing Strategies Metrics, Where Are They?" (apparently that's what the audience wanted to know too) which in turn led me back to Jeremiah's excellent article on the different sessions where Twitter played a role.
• Dan Byler gave me feedback on a preliminary version of this article and brought up the mob scene in "To Kill a Mockingbird".
• Brett Peters reminded me that I hadn't gotten around to proving my claim that it wasn't just a few malcontents; thus sending me off to create two more charts just when I thought I was done.

I've got a full day, and this post wasn't in the schedule, but then I got this twit from marshallk…

In this video, a young film-maker decides to go and recreate a test once used to help justify desegregation. Young black children are asked which doll they prefer between two almost identical dolls; the white one, or the black one. They overwhelmingly go for the white one. The results are the same when asked which is the "good" doll and which is the "bad" doll. But the heart-wrenching part comes when, having just identified the black doll as the "bad" doll, a little girl is asked which doll looks more like her. She's not happy with the choice she has to make.

I don't want to leave you completely depressed though. So here's another tale of dolls—white and black—that ends up somewhat better. Like the video above, it has a long history, and the better part takes a while to come. I wish I could find a complete version of the song on-line, but you ought to buy the album anyway, it's all excellent. In the meantime, listen to an excerpt from "Number One in America" on "Coming up for Air" and read the full lyrics below.

"Coming up for Air" (David Massengill)

Here are the lyrics, courtesy of AHistoricality.

Number One In America
© 1987 David Massengill

In Nineteen hundred and sixty-three
In my hometown, Bristol Tennessee
I was sitting on my mother's knee
Watching "Amos 'n' Andy" on TV

Amos was Santa Claus on Christmas Eve
A little girl was tugging at his sleeve
Saying, "Can I have a doll my own color please?"
He Said, "Honey, you can make believe..."

Just then came a call on the telephone
It was the mayor, he asked if my daddy was home
This was for his ears alone
Mom and me listened on the second phone

Mayor said, "The freedom Riders are on their way
And they'll be here by Christmas day
Our laws they vow to disobey
'Cause our school is as white as the milky way

Well, now we're really in a fix
We can't let 'em show us up like country hicks
But once the races mix
It's good-bye Jim Crow politics

First it's forty acres and a mule
Then they want to swim in our swimming pool
Pretty soon they'll be wanting to go to school
Where we were taught the golden rule"

Imagine them telling us how to live
Imagine them telling us how to live

Chorus:
We're number one in America
Number one in America
Beat the drum for Uncle Sam
Overcome in Birmingham
Dynamite in a Baptist church
Four teenaged girls lost in the lurch
Fire hoses and the billy clubs
Police dogs and the racist thugs
Nightriders and the lynching mobs
Lawmen say they're only doing their jobs
To stay number one in America.

Ax-handles vs. the right to vote
All white jury, that's all she wrote
Back of the bus, don't rock the boat
Separate but equal by the throat

That was twenty-odd years ago
Where's the change in the status quo?
The freedom land is lying low
it's shackled down on rotten row

The black skinned man still gets the snub
When he applies to the country club
But he still gets hired to trim the shrubs
Get down on the floor and scrub

There's a businessman out on his yacht
He's a rain or sunshine patriot
He says it's all a commie plot
To be Number One in America...

[Chorus]
The Ku Klux Klan is still around
With a permit to march in my home town
But only on Virginia's ground
The Tennesse side turned them down

The sheriff stood there with his deputies
Ostensibly to keep the peace
But he made us this guarantee
"By God, They'll not march into Tennessee!"

The network cameras were triple tiered
We laughed and cried, we hooted and jeered
But mostly we stood there unfeared
'Til the Ku Klux Klan dissappeared

In some far off distant dawn
When a Black is president and not a pawn
Will they burn crosses on the white house lawn
And talk of all the days bygone

Imagine them telling us how lo live
Imagine them telling us how to live

We're number one in America...
[Chorus]

Last Christmas Eve at the K-Mart store
A white family there, they was dirt poor
Father said, "Kids, pick one toy - no more
Even though we can ill afford..."

I watched his son choose a basketball
The oldest girl a creole shawl
The littlest girl chose a black skinned doll
And she held it to her chest and all

I watched to see how they'd react
Since they were white and the doll was black
But the mom and dad were matter-of-fact
They checked to see if the doll was cracked

So may you make a rebel stand
Where black and white go hand in hand
Until they reach the freedom land
Where the lion lies down with the lamb

Chorus:
O Number one in America
Number one in America
Beat the drum for Uncle Sam
Overcome in Birmingham
Dynamite in a Baptist church
Four teenaged girls lost in the lurch
Firehoses and the billy clubs
Police dogs and the racist thugs
Turn back the clock to Little Rock
Bought and sold on the auction block
Nightriders and the lynching mobs
Lawmen say they're only doing their job
To stay number one in America

We shall overcome someday

Sometimes companies come up with slogans or headlines that don't necessarily convey what they really intended. Of course, so long as I remember the ad, they've done their job… but still.

The one that's always bugged me particularly was U-Haul's slogan for years.

"An Adventure in Moving"

I don't know about you, but when I'm moving, the last thing I want is an "adventure." Apparently I'm not the only one.

The latest of these "slightly off" messages arrived in my email this morning. It's from VMware, a company that makes it possible for you to run multiple virtual machines in one computer.

I heard my wife say something very much like that when the server room at her work lost power. It was accompanied by several words which don't usually appear in corporate advertisements. I know what VMware is getting at, but trust me, that is not a question you usually want to hear!

I'm sure there are other, better examples out there. Leave some in the comments, or drop me an email. (I know spam is famous for this kind of unintentional humor, but let's skip those for now.)

All I really wanted to do was find the most recent email address of a friend. It was a mere matter of checking for the most recent email message from him, but he has one of those random .signature generators, and it had this interesting little poem. An hour (at least) later, here we are.

My Spill Chequer
Eye halve a spelling chequer
It came with my pea sea
It plainly marques four my revue
Miss steaks eye kin knot sea.
Eye strike a key and type a word
And weight four it two say
Weather eye am wrong oar write
It shows me strait a weigh.
As soon as a mist ache is maid
It nose bee fore two long
And eye can put the error rite
Its rarely ever wrong.
Eye have run this poem threw it
I am shore your pleased two no
Its letter perfect in it's weigh
My chequer tolled me sew.
(Sauce unknown)

So I started searching to see who wrote it. I didn't find that, but I did come across a lovely word; "oronym". It isn't in my online dictionary (it's a relatively recent neologism (another lovely word), but the Wikipedia (of course) has it. It says:

This term was coined by Gyles Brandreth and first published in his book The Joy of Lex (1980), and it was used in the BBC programme Never Mind the Full Stops, which also featured Brandreth as a guest.

Oronyms are basically homophones which span words. They work in spoken English (and often depend on dialects) because we run all our words together. The above poem uses them of course, but there's a more famous example. (This version taken from Fun With Words.) I've heard this one before, although I'd forgotten it. Once upon a time :-) I had a friend who could recite the entire piece.

An Oronym Story – Ladle Rat Rotten Hut

Even more impressive in length is the following oronym story. It is the tale of Little Red Riding Hood... but not the famous version; this one is constructed entirely from homophones: Ladle Rat Rotten Hut. This curious version was written in 1940 by a professor of French named H. L. Chace. He wanted to show his students that intonation is an integral part of the meaning of language. Try reading it out loud (best in the accent of Southern/Central USA)!

Wants pawn term, dare worsted ladle gull hoe lift wetter murder inner ladle cordage, honor itch offer lodge, dock, florist. Disk ladle gull orphan worry putty ladle rat cluck wetter ladle rat hut, an fur disk raisin pimple colder Ladle Rat Rotten Hut.

Wan moaning, Ladle Rat Rotten Hut's murder colder inset. "Ladle Rat Rotten Hut, heresy ladle basking winsome burden barter an shirker cockles. Tick disk ladle basking tutor cordage offer groinmurder hoe lifts honor udder site offer florist. Shaker lake! Dun stopper laundry wrote! Dun stopper peck floors! Dun daily-doily inner florist, an yonder nor sorghum-stenches, dun stopper torque wet strainers!"

"Hoe-cake, murder," resplendent Ladle Rat Rotten Hut, an tickle ladle basking an stuttered oft. Honor wrote tutor cordage offer groin-murder, Ladle Rat Rotten Hut mitten anomalous woof. "Wail, wail, wail!" set disk wicket woof, "Evanescent Ladle Rat Rotten Hut! Wares are putty ladle gull goring wizard ladle basking?"

"Armor goring tumor groin-murder's," reprisal ladle gull. "Grammar's seeking bet. Armor ticking arson burden barter an shirker cockles."

"O hoe! Heifer gnats woke," setter wicket woof, butter taught tomb shelf, "Oil tickle shirt court tutor cordage offer groin-murder. Oil ketchup wetter letter, an den - O bore!"

Soda wicket woof tucker shirt court, an whinney retched a cordage offer groin-murder, picked inner windrow, an sore debtor pore oil worming worse lion inner bet. En inner flesh, disk abdominal woof lipped honor bet, paunched honor pore oil worming, an garbled erupt. Den disk ratchet ammonol pot honor groin-murder's nut cup an gnat-gun, any curdled ope inner bet.

Inner ladle wile, Ladle Rat Rotten Hut a raft attar cordage, an ranker dough ball. "Comb ink, sweat hard," setter wicket woof, disgracing is verse. Ladle Rat Rotten Hut entity betrum an stud buyer groin-murder's bet.

"O Grammar!" crater ladle gull historically, "Water bag icer gut! A nervous sausage bag ice!"

"Battered lucky chew whiff, sweat hard," setter bloat-Thursday woof, wetter wicket small honors phase.

"O Grammar, water bag noise! A nervous sore suture anomolous prognosis!"

"Battered small your whiff, doling," whiskered dole woof, ants mouse worse waddling.

"O Grammar, water bag mouser gut! A nervous sore suture bag mouse!"

Daze worry on-forger-nut ladle gull's lest warts. Oil offer sodden, caking offer carvers an sprinkling otter bet, disk hoard hoarded woof lipped own pore Ladle Rat Rotten Hut an garbled erupt.

Mural: Yonder nor sorghum stenches shut ladle gulls stopper torque wet strainers.

The same Fun With Words page also then references "mondegreens" (another new word!), which are misheard lyrics.

The term mondegreen was originally coined by author Sylvia Wright, and has come to be quite widely used. As a child, Wright heard the lyrics of The Bonny Earl of Murray(a Scottish ballad) as:

Ye highlands and ye lowlands
Oh where hae you been?
Thou hae slay the Earl of Murray
And Lady Mondegreen

It eventually transpired that Lady Mondegreen existed only in the mind of Sylvia Wright, for the actual lyrics said that they "slay the Earl of Murray and laid him on the green." And to this day Lady Mondegreen's name has been used to describe all mishearings of this type!

You see these a lot on the web, when people are writing down the lyrics to their favorite songs. I remember stumbling across this one. The song is Natasha Bedingfield's "These Words". The verse goes:

Read some Byron, Shelley and Keats,
recited it over a hip-hop beat
I'm havin trouble sayin what i mean,
with dead poets and a drum machine

But the first version I found online (on some poor girl's journal) was:

Written by Ricelli and Keys
Resided in over a heartbeat
I'm having trouble saying what I mean
With dead poets and drum machines

And now I think I better get back to sending my friend that email message!

World Wide Words newsletter is a fun read. The latest typos, odd words, and questions about where phrases come from. This time there's a request for suggestions of words or phrases that have falling into disuse.