CNN's crappy rip of the image cuts off everything past October losing some of the most important data (the lead up to the holidays and Christmas Day). They also cut off the months at the bottom so there's no way to accurately tell what is happening in what month.
While it's possible that more breakups happen on Monday, I postulate that Monday is actually just the day that people are most likely to report a weekend breakup. People who break up aren't rushing to Facebook right away. A breakup can be a hugely emotional event. If it happens on Friday, Saturday, or Sunday morning, it is likely to be followed by spending a lot of time with family, very close friends, or generally being in denial. When Monday comes around, it's back to the daily grind, it's time to face reality, and telling everyone you know is one way to come to grips with the situation.
I would also argue that the pre-Christmas spike likely isn't because people are cheap. I think the main factor is that most people spend the holidays with family. Two possibilities come to mind: 1) one or both of the people in the relationship decide that the relationship is not worthwhile enough to take each other home to Mom and Dad, or 2) meeting the family over Thanksgiving leads to a breakup (it was awkward, the family didn't like the S.O., the S.O. didn't like the family, etc.).
All-in-all, I agree with the sentiment of several comments here that between the poorly-ripped image and the unjustified conclusions they jump to, the article is mostly crap.
I don't think that's the conclusion anyone is drawing. Most other articles use the phrase "announced on Monday". The journalist in the TED talk scraped status updates for "break up" or "broken up". This isn't supposed to be taken as hard evidence of anything.
All this shows is when people are statistically most likely to break up. This cannot be used to predict a specific situation (ie, "when you'll break up"), only to generalize about broad data.
Ah but it does tell you when to statistically 'work harder' on your relationship. IE Feb/March and Nov/December.
Although this can be compounded in a different way. The first break-up mountain appears after Christmas and spikes after valentines, translation: he buys crappy Christmas presents and he forgets valentines!
The second mountain is in Nov/Dec, translation: I spend all my money on her, she hasn't bought dinner in three months and now I have to get her a Christmas present too?!
Interestingly Aug/Sep/Oct were the months I noticed this year that I spent a vast majority of my time in doors. I'd gotten over the novelty of hot weather (I work outside) and had been cowering inside, which meant lots of movie theaters and dinners with the wife.
I agree that it would be more interesting to predict.
Has anyone taken Facebook data and seen if they could predict suicide or crime? Do people exhibit certain patterns before committing suicide or commiting some big crime spree?
It would also be interesting to see if it could determine if someone is cheating. Of course its harder to get data on when the cheating began to do a good analysis.
Seems flawed. If all that McCandless did was scrape status updates for "break up" and "broken up", he may well have included events referring to bands or other groups breaking up. It would have been better to look at changes in Facebook's relationship status.
Also, I'm not so sure it makes sense to assume, as the author of the article does, that breakups before Christmas have to do with money. If the data is even valid, then it's quite likely that breakups occur before Christmas because people don't want to go through the charade of spending Christmas together and possibly with each others' families if the relationship isn't going anywhere.
It would be interesting to be able to segment the graph by how long the relationship that was just broken up--mostly as a proxy for how long someone takes to recover from having broken up. Thus, you can find the best times of the year to be looking for singles.
If everyone took the same amount of time to get over the last relationship (which is not true), then we can just start looking for singles just a couple months after the spikes.
Is it possible they really meant "over 10,000 status updates" that include one of the phrases in question? Broken down yearly, 10,000 samples is an average of somewhere around 25 samples a day, and I'd imagine that on average far less than 1/25 of Facebook status updates are about breakups, so there would be only a handful of days with any hits, let alone enough to pick out a "Monday effect" or anything like that.
If it's 10,000 that match the query, then that's a more reasonable amount of data. Of course, the interpretation is still pretty shallow, and mere regex-matching for those phrases could be getting a lot of other crap, but at least it's a (just barely, given that they want to break it down day-by-day) decent sample size.
Right, the article examines breakups ex-post. That is, they can only identify breakups after they happen.
Ex-ante, though...I remember hearing somewhere that Zuckerberg used to predict when people were about to split, because they could see whose profiles you were looking at.
If you started checking out potential partners with a high frequency, and you were in a relationship, Zuck et al. knew it wouldn't last long.
[+] [-] vaporstun|15 years ago|reply
Here is their source site which has a better version: http://www.geekosystem.com/facebook-breakup-graph/
[+] [-] chronomex|15 years ago|reply
[+] [-] BrandonM|15 years ago|reply
I would also argue that the pre-Christmas spike likely isn't because people are cheap. I think the main factor is that most people spend the holidays with family. Two possibilities come to mind: 1) one or both of the people in the relationship decide that the relationship is not worthwhile enough to take each other home to Mom and Dad, or 2) meeting the family over Thanksgiving leads to a breakup (it was awkward, the family didn't like the S.O., the S.O. didn't like the family, etc.).
All-in-all, I agree with the sentiment of several comments here that between the poorly-ripped image and the unjustified conclusions they jump to, the article is mostly crap.
[+] [-] brianpan|15 years ago|reply
[+] [-] michaelchisari|15 years ago|reply
[+] [-] electromagnetic|15 years ago|reply
Although this can be compounded in a different way. The first break-up mountain appears after Christmas and spikes after valentines, translation: he buys crappy Christmas presents and he forgets valentines!
The second mountain is in Nov/Dec, translation: I spend all my money on her, she hasn't bought dinner in three months and now I have to get her a Christmas present too?!
Interestingly Aug/Sep/Oct were the months I noticed this year that I spent a vast majority of my time in doors. I'd gotten over the novelty of hot weather (I work outside) and had been cowering inside, which meant lots of movie theaters and dinners with the wife.
[+] [-] kenjackson|15 years ago|reply
Has anyone taken Facebook data and seen if they could predict suicide or crime? Do people exhibit certain patterns before committing suicide or commiting some big crime spree?
It would also be interesting to see if it could determine if someone is cheating. Of course its harder to get data on when the cheating began to do a good analysis.
[+] [-] aasarava|15 years ago|reply
Also, I'm not so sure it makes sense to assume, as the author of the article does, that breakups before Christmas have to do with money. If the data is even valid, then it's quite likely that breakups occur before Christmas because people don't want to go through the charade of spending Christmas together and possibly with each others' families if the relationship isn't going anywhere.
[+] [-] jhuckestein|15 years ago|reply
Other than that, the title is total linkbait.
[+] [-] c4urself|15 years ago|reply
[+] [-] iamwil|15 years ago|reply
If everyone took the same amount of time to get over the last relationship (which is not true), then we can just start looking for singles just a couple months after the spikes.
[+] [-] tyrmored|15 years ago|reply
Sample size sucks too. Of only 10,000 status updates, how many would actually include those two phrases? I call bullshit.
[+] [-] ewjordan|15 years ago|reply
If it's 10,000 that match the query, then that's a more reasonable amount of data. Of course, the interpretation is still pretty shallow, and mere regex-matching for those phrases could be getting a lot of other crap, but at least it's a (just barely, given that they want to break it down day-by-day) decent sample size.
[+] [-] achompas|15 years ago|reply
Using that ratio (50 updates/month), we can estimate that their 10,000 status updates cover 16 people over a year.
My first instinct was "why not?" but thinking about this more, that looks kind of thin. Of course, I'm assuming a lot of things away...
[+] [-] stephencelis|15 years ago|reply
What are the rules re: reposts of vias?
[+] [-] achompas|15 years ago|reply
Ex-ante, though...I remember hearing somewhere that Zuckerberg used to predict when people were about to split, because they could see whose profiles you were looking at.
If you started checking out potential partners with a high frequency, and you were in a relationship, Zuck et al. knew it wouldn't last long.