Tag Archives: internet

What if you left your kid alone with YouTube

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YouTube is the educator entertainer that never sleeps. One video leads to the next, literally forever. (YouTube does have a kids channel which is supposed to be a safe space for kids.) They have “YouTube Kids,” which was supposed to help reassure parents. But if your kid is at a random computer and just goes to YouTube.com, or clicks on a link and ends up there, they’re off down Recommendation Alley.

In response to fears that YouTube was promoting bad things to children, unintentionally or not, and thinking about a possible sociology class exercise, I decided to do an exercise where I start from a Disney princess video and then select from one of the top-10 recommended videos on each page to try to get to things that are bad for children. (In the possibly-vain hope that my experiment wouldn’t be contaminated by my own use history, I used an incognito window without logging in to Google.) My goal was Nazi propaganda, and my strategy was to aim for adult stuff, then look out for disturbing, racist, or violent content. As children do.

I gave up after 113 videos, without getting to Nazi stuff. I would love to know — as YouTube surely does — how children really use YouTube when no one’s looking. I know from limited experience they click around a lot — covering a lot of videos in a short time — and they don’t vet their “content” carefully. So this seems like a plausible browsing session. Anyway, still thinking about how to do something like this, and thought I’d share my notes here:

How fast can I get to Nazi stuff from “Disney kids” using videos from the first 10 recommended? (Spoiler, I couldn’t, but still.)

After searching for “Disney kids,” I chose this innocent Disney Princess video, and starting clicking on recommendations.

  1. Kids Makeup Disney Princesses Pretend Play with Cleaning Toys & Real Princess Dresses https://www.youtube.com/watch?v=yl-dYcHTAYg
  2. Emily Became a Princess-Real Princess Dresses https://www.youtube.com/watch?v=euGnK3OGU_A
  3. PRINCESS SCHOOL TEST 🎓 Lilliana Helps Isabella To Cheat! – Princesses In Real Life | Kiddyzuzaa https://www.youtube.com/watch?v=dD-bDjWwtjA
  4. Kaycee and Rachel in Wonderland # 26 https://www.youtube.com/watch?v=yhuGyvxZb3Y
  5. 24 Hours in Box Fort Jail Challenge! 24 Hour Challenge with No LOL Dolls https://www.youtube.com/watch?v=noJv8tV0FF8

By #6 I’ve gotten as far as icky

  1. Father & Son PLAY DON’T STEP IN IT! / Avoid The Poo! https://www.youtube.com/watch?v=CoNBU6alsjA
  2. Escaping Hello Neighbors Maximum Security Box Fort Prison / Jake and Ty https://www.youtube.com/watch?v=3BPdjI697GU
  3. 9 Weird Ways To Sneak Food Into Class / Summer Pranks! https://www.youtube.com/watch?v=99n2OLtIUwY

A reference to a shooter video by #9. This leads into family conflict…

  1. FORTNITE DANCE CHALLENGE !! In Real Life With Ckn Toys https://www.youtube.com/watch?v=k96FHPngnwU
  2. FORTNITE Dance Challenge! IN REAL LIFE | NINJA KIDZ TV https://www.youtube.com/watch?v=dBR0onumDq4
  3. Sister VS Brother Battle! https://www.youtube.com/watch?v=p8AERX2Rolw
  4. Katherine is a Barbie! https://www.youtube.com/watch?v=pK7idnorApQ
  5. How To Remove White Marks From Your Baby Alive! Commercial Style https://www.youtube.com/watch?v=FOOWYej1KNc
  6. Lalaloopsy school adventure episode 1: bullying! https://www.youtube.com/watch?v=zvJIODxOTeo
  7. Sketch Smell Challenge https://www.youtube.com/watch?v=Bjj8O8rhedU
  8. NEVER HAVE I EVER!! https://www.youtube.com/watch?v=Ix5_v8uLiuk
  9. Older Siblings vs Younger Siblings!! Sisters Trinity and Madison https://www.youtube.com/watch?v=QqX3nnL2fOE
  10. EXPECTATIONS vs REALITY of Having a Sibling https://www.youtube.com/watch?v=U88fcNfrKXQ
  11. EXPECTATION vs REALITY OF BEING A PARENT!!!! https://www.youtube.com/watch?v=9SIXQjkf4Y4
  12. Amelia and Avelina beach vacation adventure https://www.youtube.com/watch?v=m80SpeAncyI
  13. HOTEL HOUSEKEEPER CHASED BY COPS AT RESORT!!! POLICE UNDERCOVER https://www.youtube.com/watch?v=44bft2AfUWU
  14. A CREEPY STALKER FAN STALKS ME OUTSIDE MY OWN HOUSE *HE HAD MY PHONE NUMBER* https://www.youtube.com/watch?v=CrNLHlL2OEE
  15. Is Nicole a Zombie, Forever? https://www.youtube.com/watch?v=kDouUbA4oME
  16. The Girl Who Collects Cockroaches | My Kid’s Obsession https://www.youtube.com/watch?v=L9W7J7iW39E
  17. QUEEN BABY: Bath Time https://www.youtube.com/watch?v=arSknXl8sko
  18. Power Tool Wins and Fails https://www.youtube.com/watch?v=j2bAlONGEPw

First real violence by #27. Not that bad, just a skateboard injury, which introduces the people-behaving-badly-in-real-life genre

  1. Skateboarder Crashes into Kid https://www.youtube.com/watch?v=ec4LNeV4CTQ
  2. Lady Yells at Kid on Alpine Slide in Winter Park CO (Fight) https://www.youtube.com/watch?v=D7c82c7ay5s
  3. ex-wife acting out in front of kids https://www.youtube.com/watch?v=toz-JgqhI94
  4. Baby Mama manipulating again (Arizona) https://www.youtube.com/watch?v=lcwFYTN_w7o
  5. CPS NOT WANTING TO GO ON CAMERA https://www.youtube.com/watch?v=juahHP_xYDc
  6. CPS murdered my family https://www.youtube.com/watch?v=dpIihXia0Yo
  7. CPS Supervisor Calls Parents “White Trash”!!! https://www.youtube.com/watch?v=AnPmQgekBog
  8. Crazy lady at skate park https://www.youtube.com/watch?v=9zRmrIG3ehk
  9. Crazy lady yells at kids for standing on table https://www.youtube.com/watch?v=RJkXQvuTG1A
  10. Lady yells at kids https://www.youtube.com/watch?v=C141zVXqrY0
  11. Super mad bus driver and kid trys to escape https://www.youtube.com/watch?v=O0hhaCVP1Uc
  12. BUS DRIVER REFUSES TO LET CHILDREN ON BUS!!! https://www.youtube.com/watch?v=uDJOmzwBQv4
  13. Bus Driver Kicks Girl Out of Bus.Miles Away from Home https://www.youtube.com/watch?v=Xvj4Kg6Ll9I
  14. Mean bus driver https://www.youtube.com/watch?v=VSrMgZe5UC0
  15. Creepy little girl brings me to bathroom stall and locks the door https://www.youtube.com/watch?v=hjyb1IMeEpA
  16. My humps remix (Barbie and crazy) https://www.youtube.com/watch?v=ZX150oaD2H0
  17. Two girls fighting https://www.youtube.com/watch?v=QNpq1GnCaCc
  18. Two boys and two girls fighting https://www.youtube.com/watch?v=0czg5hVzld0
  19. This wat happen when a 2nd and 4th grader fight https://www.youtube.com/watch?v=P8vn7wEQ_Lk
  20. 3rd Grade fight in school https://www.youtube.com/watch?v=yDAcnde8P3s
  21. Bullying 3rd Grade https://www.youtube.com/watch?v=Z6uOT-A3ZaE
  22. WORLD’S MEANEST LITTLE GIRL – IDIOTS ALWAYS ASK #12 https://www.youtube.com/watch?v=V22Xw0y7LsY
  23. Little kids fight https://www.youtube.com/watch?v=h50amm_gXCY
  24. 8 vs 10 year old fighting https://www.youtube.com/watch?v=amovxJrgZx4
  25. 8 year old vs 13 year old fighting https://www.youtube.com/watch?v=jNLqypr2Xuc

Trying to get out of the kids-fighting loop, I chose this one, which led to stuff for parents…

  1. Kid Pukes at Dentist after Getting Mold Removed https://www.youtube.com/watch?v=tT6pw58CWKU
  2. 10 year old Isabella shouldn’t know The ‘C’ Word #LyttleFight https://www.youtube.com/watch?v=CcIVTZxnTkU
  3. Slap Her https://www.youtube.com/watch?v=f6CDvSDkeAM
  4. Doll test – The effects of racism on children (ENG) https://www.youtube.com/watch?v=QRZPw-9sJtQ
  5. Disturbingly Racist Moments in Cartoons https://www.youtube.com/watch?v=cftUIdSr_T8
  6. Top 10 Insanely Racist Moments In Disney Movies That You Totally Forgot About https://www.youtube.com/watch?v=YKWGQyz-oLw
  7. 10 Dark Theories About Dead Disney Characters https://www.youtube.com/watch?v=rbfpJ7gZl44
  8. Sausage Party: 10 Important Details You Totally Missed https://www.youtube.com/watch?v=bSiQkslhg7A
  9. 15 Moms You Won’t Believe Actually Exist https://www.youtube.com/watch?v=0OlmYhaGkGI
  10. Most Inappropriate Children Coloring Book Drawings! https://www.youtube.com/watch?v=UD81kn5L7O0
  11. Bunk’d Stars ★ Before And After https://www.youtube.com/watch?v=jhm01EsBEkU

Somehow this led to freaky or scary images and general danger…

  1. 10 STRONG KIDS That Can Lift More Than You https://www.youtube.com/watch?v=ZoG5_LWChVQ
  2. World’s Strongest Kids Girl https://www.youtube.com/watch?v=EV39CR7v7gA
  3. Remember This Viral Photo Of A Nigerian ‘Witch’ You Should See Him Now https://www.youtube.com/watch?v=Y7zN75E7rOg
  4. 10 SHOCKING Incidents When Kids Left Alone With Pets https://www.youtube.com/watch?v=B56hVR8QhoE
  5. 10 Times TOYS Got Kids In TROUBLE With Police Officers https://www.youtube.com/watch?v=R-XH7ylO5ls
  6. ILLEGAL and BANNED Fidget Spinners https://www.youtube.com/watch?v=NLG4sL_MF5Q
  7. WORLD’S MOST DANGEROUS FIDGET SPINNERS!! https://www.youtube.com/watch?v=qZiRxVFdDsw
  8. NAUGHTY BABY DOES SECRET TOY RITUAL AND SUMMONS GHOSTS FROM CARTOON!! || Baby Hands Gameplay Part 15 https://www.youtube.com/watch?v=rb0wgPFYAIQ
  9. Creepy texts from babysitter.. | TEXT STORY REACTION https://www.youtube.com/watch?v=oRFccgkGQ9w

No idea why this Trump parody was here but I thought it might lead to more political content. Instead it took me into a video game loop, which I only got out of by going back to bad parenting…

  1. SAVE TRUMP! \ Mr. President https://www.youtube.com/watch?v=0ClhcQYRqig
  2. Realistic Minecraft – Highschool Girlfriend ❤ https://www.youtube.com/watch?v=kR98shYOpYo
  3. You Can’t Say No To Ella! https://www.youtube.com/watch?v=F66gmHWTks8
  4. CAN PARENTS GUESS WHAT THEIR KID DOES WITH 100 DOLLARS? Ep. # 2 https://www.youtube.com/watch?v=gVjHOyKQn3M
  5. What would your kid do if they found a gun? https://www.youtube.com/watch?v=VkcfQTavqyk
  6. Kids found home alone https://www.youtube.com/watch?v=cM7a_BppUIE
  7. 19 kids found alone in filthy, hot Kentucky home https://www.youtube.com/watch?v=_i-7BbEr9YI
  8. Baby Buried Alive https://www.youtube.com/watch?v=d_1ykYYRcyI
  9. Newborn baby found abandoned near Tampa intersection https://www.youtube.com/watch?v=NDwkUG0qQNI
  10. angry lady yells at kid for no reason… https://www.youtube.com/watch?v=FNv-ToebU8U
  11. Man slaps crying baby in it’s mothers arms on Delta airline flight, calls it n-word https://www.youtube.com/watch?v=jrhZuG3zAGM
  12. Boy Passes After Putting Blue Stain In Carpet. 14 Years Later Mom Floored By Real Meaning https://www.youtube.com/watch?v=wTCaZzsQPMg

Then we’re back to freak shows, and from there to child brides, poverty, and then – fake poverty…

  1. she was born with an elephant’s trunk, this is what they did to her… https://www.youtube.com/watch?v=WflgtnSvtMs
  2. Worst Bug Invasions Ever https://www.youtube.com/watch?v=5Wl7aF4qUyI
  3. Mom Thinks She’s Having Twins, But Drs Quickly Learn She’s Making History With Rare Delivery https://www.youtube.com/watch?v=EfVdG6crFAg
  4. Child Marriage in Ethiopia’s Amhara Region HD https://www.youtube.com/watch?v=ZYk37j9g300
  5. Mamoni’s Story: The Child Bride https://www.youtube.com/watch?v=TCxcfEOEMoI
  6. The Ugly Face of Beauty: Is Child Labour the Foundation for your Makeup? (RT Documentary) https://www.youtube.com/watch?v=AOpZkstB5jc
  7. The Poorest of the Poor – On the Edge of Europe https://www.youtube.com/watch?v=fEZSjtpHo44
  8. Fake Homeless People CAUGHT On Camera And EXPOSED! https://www.youtube.com/watch?v=KbYRqSeqJIg

Fake stuff leads to the “what would you do” genre…

  1. White Woman Introduces Asian Fiance To Disapproving Parents | What Would You Do? | WWYD https://www.youtube.com/watch?v=Kom9wMpLIzE
  2. Christian Discrimination for Praying in Public | What Would You Do? | WWYD https://www.youtube.com/watch?v=s4SkVFrQFW4
  3. Foster Care Cruelty | What Would You Do? | WWYD | ABC News https://www.youtube.com/watch?v=Bvn91N92VCw

And from there back to suffering children.

  1. Foster Care Support – They Come In The Night – With Nothing! https://www.youtube.com/watch?v=S7hICHJOiAI
  2. Annie’s Story (Neglect) https://www.youtube.com/watch?v=6lB2iujfv5A
  3. Russian Orphans – Master Thesis Documentary https://www.youtube.com/watch?v=adPF39ozmMs
  4. Inside AK Orphanage https://www.youtube.com/watch?v=mSy6vc7Jijo
  5. Nigeria Beggar Abandons 3 Babies on Street https://www.youtube.com/watch?v=GyeE1K6yziU
  6. Hungry Kids In Africa https://www.youtube.com/watch?v=U_Zt_J0UEb4
  7. child survival in Africa | survive a tout prix https://www.youtube.com/watch?v=nrDuVwCSF94
  8. AIDS Orphans in South Africa https://www.youtube.com/watch?v=0oSR9SM0Se0

Don’t know why sassy girl was here, but it got me away from sad orphan stories and back to bad parenting…

  1. Sassy little girl blocks the slide at the zoo https://www.youtube.com/watch?v=dQ8VPezlOg0
  2. MOST SPOILT BRAT KIDS https://www.youtube.com/watch?v=1BqTmuuzXek
  3. Most Spoiled Kids Compilation 6 https://www.youtube.com/watch?v=bexSD2uD3Ms
  4. Kids Who Are Crying For The Most Ridiculous Reasons https://www.youtube.com/watch?v=RzrqyB62IVA
  5. What Would You Do: Mother Uses Harsh Punishments on Son | What Would You Do? | WWYD https://www.youtube.com/watch?v=RpiSeuSU8EE

Bad parenting is related to sappy family stories, like soldier homecomings, which led to family separations…

  1. Soldiers Coming Home || Emotional Compilation https://www.youtube.com/watch?v=oT8j2Vm8PaY
  2. Military Homecoming – Meeting Baby Elijah https://www.youtube.com/watch?v=oIOkrjMrW_I
  3. Babies Behind Bars – Part 2 https://www.youtube.com/watch?v=kWaZ34Vmaf4
  4. Mom Puts Baby Girl To Bed. Hours Later Hears Screaming & Realizes Hidden Danger In Her Room https://www.youtube.com/watch?v=dvg1nSpmdIE

Which led to bad parenting again…

  1. Police officer finds pregnant mom and toddler asleep on sidewalk https://www.youtube.com/watch?v=5cHzrUBtnZg

And finally back to Disney Princesses. Phew!

  1. Moms Dress Like Disney Princesses For Maternity Photos https://www.youtube.com/watch?v=cW15sbLH-Ts

 

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Year-end report and most popular posts, 2014

A few days ago Family Inequality reached 1 million total views.

After more than doubling in 2011 and 2012, average daily traffic on Family Inequality only grew 41% in 2013, and now in 2014 it grew only another 25%. The declining growth rate may in part reflect slower growth in the number of American Internet users The blog’s traffic grew faster than Facebook (8% growth in North American active users) and Twitter (14% growth in timeline views) in their last 12-month periods.

Facebook and Twitter are the greatest click-contributors after search engines, with Facebook bringing 1.7-times more readers than Twitter. Sociologists in particular come from, and share to, Facebook.

Here’s the word cloud of search words used to find the blog this year. This time I broke up the phrases so, for example, “unbelievable sex” yields two separate entries. I deleted family and inequality, which were the most popular (click to enlarge).

2014cloud

These were most popular posts I wrote this year:

10. Turns out marriage and income inequality go pretty well together. Inequality among married-couple families is high, and it’s rising faster than inequality among single-parent families.

9. The less things change, the more they stay the same: Michigan edition. The representation of Black students at the U. of Michigan has fallen 50%.

8. The most comprehensive analysis ever of the gender of New York Times writers. Analysis of more than 21,000 NYT articles found that women wrote 34% of them. And you’ll never guess what sections they’re in (actually, you will).

7. Movie dimorphism update: How to Train Your Dragon 2 edition. Another year, another hand-size dimorphism extravaganza in animated movies.

6. Getting beyond how the ‘Factual Feminist’ is wrong about the prevalence of rape. On the idea that feminists exaggerate the problem of rape, and a deeper critique.

5. It’s modernity, stupid (Book review of The Sacred Project of American Sociology, by Christian Smith). He can’t find a way to convince everyone else that they’re the ones who are crazy. Inevitably, out of desperation, he starts to write in italics.

4. What a recovery looks like (with population growth by age). The simple observation that you need to adjust for population growth and change when evaluating the recovery. With graphs.

3. Is the price of sex too damn low? A critique of the very wrong and extremely sexist video by Mark Regnerus.

2. Especially if they’re Black: A shortage of men for poor women to marry. The left-right debate about marriage stays away from race. It shouldn’t.

1. Does sleeping with a guy on the first date make him less likely to call back? A simple data simulation shows how the popular admonishment — he won’t call because he thinks you’re disgusting, so shame on  you — may be completely wrong.

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ASA meeting Twitter network graph

The American Sociological Association meetings, which ended earlier this week, had a rollicking good Twitter stream. Now Marc Smith has analyzed the tweeters who used the hashtag #asa14 (and related), and their interactions, to produce network graphs of the meeting’s tweeted undercurrent. I looked through one of the graphs, which I’ll describe briefly.

Smith used NodeXL, and generated a whole gallery of graphs. Just looking in your browser is difficult because the resolution is too low to identify people, but you can download the giant Excel files he made, or use the interactive graphs which allow you to hover over points and see their handles. That’s how I figured out the following graph, which represents 18,000 tweets from Sunday and Monday, the middle of the conference (click to enlarge, but it won’t help that much). Details here, my description below.

Graph-25487The top left, G1, is the heaviest traffic. This was a lot of leftists in active discussions of Ferguson, Missouri, Mike Brown, and Alice Goffman (and her book On The Run). At the center of that mass seems to be Jessie Daniels from CUNY (who describes herself, fittingly, as an instigator), UT-Austin sociology, Conditionally Accepted, Dr. Compton, and C.J. Pascoe, among others. I can’t find the dot for Tressie McMillan Cottom (Tressiemcphd) — who has the highest betweenness centrality of any individual on the graph, and was the most frequently replied-to tweeter — but it’s probably in G1 somewhere.

Moving clockwise, the next cluster (G3) is centered around the official feed of the ASA, @ASAnews, with a lot of tweets about the conference theme, publishers and their booths, and journals.

Clockwise to G5, you get another cluster with a lot of Mike Brown and Ferguson, but this one more focused on education and academia, including Lean In. At the center of G5 is Sara Goldrick-Rab.

The top right, G6, is where I ended up. It has several lose center points, including me (familyunequal), Tina Fetner, and two people who tweeted ASA content that got picked up by a lot of non-sociologists: Mark Abraham (urbandata) and Str8Grandmother. Also up there is Karl Bakeman (my editor at Norton), the Norton sociology feed, and Contexts magazine.

Next on the far right is G10, which has a lot of critical race discussion (#troublewithwhitewomen), as well as information technology. I can’t tell the theme of G9, which includes Lisa Wade and Nathan Palmer (sociologysource).

The orange oval in G7 is centered around the Émile Durkheim feed (“Invented Sociology, and don’t let any Germans tell you otherwise”). This was probably his most popular tweet this time out, with going on 100 retweets:

durkheimcup

In fact, the graph data shows that the G7 sector basically comprises the community formed around this tweet.

The bottom center sector, G4, clusters around Think Progress. Note the strong ties to the top left, where the Ferguson traffic was heaviest. G4 is a key group for transmitting leftist politics into and out of ASA. The feminist Leta Hong Fincher is the node that connects this group to that fan of people off the bottom right of the cluster.

Finally, the bottom left group, G2, is centered on education and technology, with clusters around Liz Meyer, Marc Smith, Gina Neff, and others I’m not familiar with.

So

There are lots of social layers and clusters across the ASA, which could be grouped by specialty, department, age, race/ethnicity, nationality, sexuality, and so on. The Twitter network just happens to leave an easy data trail. I mention all these individuals not to play into a star system, but because it’s easier to name someone than to attempt to categorize them. I’m open to other interpretations of this graph.

I’m getting very sappy in my old age about my love for sociology and sociologists. But as I look over these figures, I think that if I had to pick 5,000 people to spend a weekend with, who all had only one thing in common, I think ASA members was a good choice.

 

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The couple-height story

My post the other day on height differences between married men and women, which appeared here and on The Atlantic site, drew record clicks to the blog. They mostly originated from this Jezel post by Tracy Moore, which reports about 100,000 readers and more than 1,000 comments on their post.

The UK’s Daily Mail Online also picked up the story, which is understandable since the original research came from a British sample. But I was most impressed by their re-purposing of the figures I made — until I realized they botched it. I don’t know how they made these, since I didn’t include the numbers behind the charts. Here is my figure (on the left), with their adaptation.

dm-height1

I like the bathroom icons showing which spouse is taller. Anyway, that’s the actual distribution, and it seems right. They did the same thing with the randomized height distribution:

dm-height2And that seems OK, too. But then for superimposing them, they shrunk the actual distribution down to the scale of the randomized one — I guess not realizing that the y-axis went higher on the actual distribution. As a result, the point was totally lost:

dm-height3It’s a lot of trouble to go through in order to get it wrong in the end. I wonder why they didn’t just rip off the original figures, like they did with the text, re-writing most of the post like this:

I said: I made 10 copies of all the men and women in the data, scrambled them up, and paired them at random. Most couples are still husband taller, but now 7.8 percent have a taller wife – more than twice as many.

They said: To do this he made 10 copies of all the men and women in the data, scrambled them up, and paired them at random. Most couples still had a taller husband, but 7.8 per cent had a taller wife – nearly twice as many…

And so on through most of the post. (I also don’t know why they changed my “more than twice as many” to “nearly twice as many,” since I was comparing 3.8 with 7.8. I have checked this a couple of times now and I’m pretty sure 3.8 * 2 = 7.6.)

It’s an interesting (unimportant) case of the blogosphere’s frequently-encountered overlap between free publicity (they publicized the post), plagiarism (they claimed words written by others as their own) and copyright infringement (they republished someone else’s work without permission).

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Online dating: efficiency, inequality and anxiety

My contribution to an Atlantic.com forum on online dating, originally published here.

White men are the most sought-after group on OkCupid, while black women are the least.

the dating game 615.jpg

ABC

Even in Super Sad True Love Story—the Gary Shteyngart novel where everyone wears an “äppärät,” a device around their necks that broadcasts to everyone around them their credit history, income, cholesterol, and how attractive they are compared with everyone else in the vicinity—even in that world people fall in love. And we’re not quite there yet.

Executives in the middle of a growing business can be forgiven for overstating trends—as can individuals used as anecdotal launching pads for trend pieces—but readers should take it a little slower. So rather than go right to “online dating is threatening monogamy,” as Dan Slater argues in his article in The Atlantic magazine, maybe we could agree with the less alarmist conclusion that people who engage in rapid serial online dating are probably less likely to make commitments because they won’t settle down. And then we could look at how that trend fits in with the larger questions we face.

Efficiency

First, I’m skeptical of the claim that, as one executive put it in the article, “the market is hugely more efficient” as a result of online dating. Plenty of the people who spend all day online are interacting with real people less than they used to. They waste huge amounts of time dealing with online daters who lie, mislead them, stand them up, or dump them on a moment’s notice.

In a terrific 2003 New York Times article by Amy Harmon, a fourth-grade teacher, retold the statistics of her four-months of online dating: messages exchanged with 120 men, phone calls with 20, in-person meetings with 11—and 0 relationships. That’s not efficient at producing relationships—but it is efficient at producing anxiety. My favorite sentence from that article:

It’s amazing how all women say they’re slender when a lot of them are overweight,” said one 79-year-old Manhattan man who lists himself as 69 on his Match.com profile.

On the other hand, back in the days of dating, women entering college in the 1950s reported an average of about 12 dates per month (three per week) with five different men. These women were grossly outnumbered in college, and most women didn’t go to college, so it wasn’t a system for the whole society. But it tells us something about efficiency: Since dating reliably ended in marriage within a few years, it was pretty efficient, but that’s because of the attitude and expectations, not the technology.

For people who are intent on being choosy, online dating might be more efficient than meeting people in person, but people in urban areas have been finding alternative partners for a long time. For example, we have known for several decades that people are more likely to divorce when they are presented with more, or better, alternatives. In the 1990s researchers discovered that “the risk of [marital] dissolution is highest where either wives or husbands encounter an abundance of spousal alternatives.” They concluded, “many persons remain open to alternative relationships even while married.” This has been shown not only by looking at the composition of the surrounding urban area, but also by simply comparing the divorce rates of people who work in gender-mixed versus gender-segregated occupations (the former are more likely to divorce). Marriage hasn’t been unleavable for quite a while.

Still, maybe online dating speeds up the turnover process, and this might contribute to the trend of delaying marriage going on since the 1950s.

Inequality

Second, I think it’s possible that—in addition to undermining what’s left of monogamy—the spread of online dating will widen some social inequalities. Remember those left behind by Jacob’s wandering webcam eye in the article? When he wanders off to a new partner, he leaves one behind. She might or might not have the same options to exercise. In this rapid-turnover process, the richer, better-looking, healthier, better-lying, etc., might make things miserable for more people than they used to be able to. Jacob’s efficiency might be their wasted months and years.

But remember, divorce rates have probably been falling more or less continuously since about 1980. And it is the less well-off who have been marrying less and divorcing (relatively) more. The people who are divorcing more—or marrying less—are the ones who aren’t going to do as well in the “efficient” competition on dating sites. They aren’t going to gain much from this onlinification.

A few years ago I reported on an amazing analysis of message patterns by the dating site OkCupid. It showed that black women got the lowest response rates to their messages on the site. Here is the pattern—with each cell showing the percentage of men replaying to messages from women, according to the race of the sender (left) and the recipient (top). For example, black women got a 32 percent response rate from white men, whereas Middle Eastern women got a 47 percent response rate from white men.

cohen_onlinedating.png

If this system is efficient at finding perfect matches, it is also efficient at sorting people according to existing social hierarchies—applying what Alexis Madrigal in The Atlantic called “algorithmic perversity.” Some people will use online dating to constantly trade up—maybe ditch a sick or unemployed spouse—and that will also speed up other processes, like the widening of social inequality.

Reflexive responses

There’s no reason not to overhype a trend. The reward in attention is much greater than the penalty down the road if it turns out you’re wrong. But put this in perspective. Granting that the situation may be changing fast, let’s just consider that in 2006 the Pew Center published a report on its survey of 3,215 adults. Of those who were married or in a committed relationship, 3 percent had met their partner online, and of those, just 41 percent—or 1 percent of the total—met through a dating website.

So online dating may be affecting a fair number of Jacobs and their partners, but it hasn’t remade all of our relationships yet. Articles like this, however, increase the pressure on people to consider—and reconsider—their choices. The same happens with articles about parenting, or biological clocks, or cohabitation—all the family decisions for which choices appear to be multiplying. And it may be true that people are less content when they have more choices—but I bet it’s also true that the effect is magnified when the extent of their choices is hyped and rehyped, and evaluated by competing experts.

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Recession, divorce?

I haven’t yet seen any definitive evidence that the recession has had an effect on divorce rates. But if I’m going to pick on other people for this, I should offer a few ideas.

In a previous post I cited a lot of reasons to expect divorce would increase as a result of family stress and instability. Others claim these hard times are bringing couples together in the face of adversity. And either – or both – of these influences is woven into the long term trends in divorce. Here are three graphs looking at the question.

Long-term trends

The overall divorce trend doesn’t seem to be moved much one way or the other by recessions (shown in blue), at least for the last 60 years:

That national data is only available through 2009, so a little early to see a major effect. Still, no disruption of the trend at the national level, just a continuous decline in the divorce rate. (Here’s a great recent Census report on divorce trends.)

State patterns

The official divorce rates are available from 38 states, but they’re only considered reliable through 2008 so far. By putting together two years of changes — 2006-2007 and 2007-2008 — this figure has 76 dots (two for each state), showing whether changes in divorce are related to changes in unemployment rates. This gives a rough idea of the relationship between how hard states were hit by the recession and any changes in divorce:

I made the dots larger according to the population sizes, and weighted the red trend line so that the larger states have more effect (this positive relationship is statistically significant at the 99% confidence level). This is just a start, but it leans in the direction of unemployment increasing divorces. At least it doesn’t look like the recession is driving divorce down.

Google

Finally, what about divorce on the American brain? For a glimpse inside, we turn to Google trends. If it works for the flu, it might work for divorce, too. Here are the trends for “divorce attorney” and “divorce lawyer”:

The trend for both searches looks basically flat except for seasonal variation, and some turbulence in 2008. But nothing to suggest a major trend one way or the other. (You can play with these yourself, starting with mine, here.)

My conclusion so far: no national evidence of a recession effect on divorce yet, but some suggestive hints worth keeping an eye on — leaning in the direction of recession causing more divorces — in opposition to a long-term downward trend. Maybe if and when the housing market loosens up more unhappy spouses will take the plunge and move out. The divorce rate may continue to fall, as it has been since the early 1980s, but that doesn’t mean this recession has a “silver lining” for families.

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Find that food stamp spike graphic meme

Classic axis abuse, and a memetic mystery unsolved.

My friend Danielle pointed me toward this graph from Michelle Malkin‘s blog yesterday. (I never heard of her, so I’ll use her identification: “mother, wife, blogger, conservative syndicated columnist, author, and Fox News Channel contributor.”)

For Malkin, this is evidence of “the Obama FoodStampCorps’s mission to enroll countless more food-stamp beneficiaries.”

From her own link trail, it looks like Malkin may have gotten the graph here, though in an earlier post she linked to a version here, so who knows. Trying to find the real source of it, I quickly discovered that it has been posted dozens, maybe hundreds, of times in different versions as new monthly data become available.

Case report

So this is the first case report on a food stamp spike graphic meme. Until I can examine a live version I won’t know for sure, but it seems to trigger a proto-oncogene that is carried on the conservative cultural-chromosome. (It comes with commentary like this: “Worst. Economy. Ever. Also, not coincidentally … Worst. President. Ever.”)

The graphic is usually credited to the Supplemental Nutrition Assistance Program (SNAP), to USDA or both (as in this above example). But although SNAP publishes these data, I don’t believe SNAP produced the figure. Conservative bloggers attribute it to SNAP, presumably because whoever makes the graph puts that label on it, but they don’t say where they got it.

If someone else can trace this, I would love to know who produces it. Clue: the file is often called something like “food-stamps.png”. You can see a lot of them by image-Googling this: [food-stamps.png snap “food stamp participation“]

Anyway, the obvious problem is classic x-and-y axis truncation, which we’ve seen before. The lowest point on the chart is 26 million, and the earliest point is in 2007. The same source includes data back to 1969, so it’s easy to widen the angle (using years instead of months):

Source: My graph from SNAP enrollment data and Census population estimates.

The x-y distortion is what gives the meme its distinctive appeal to the conservative cultural-oncogene, which strongly favors simple linear forms. The meme’s molecular structure allows it to snap into what is normally a relatively-harmless gullibility gene, triggering uncontrolled replication. Here’s an illustration:

Showing the additional ranges on the graph doesn’t change the fact that Food Stamps really is serving a record number of people, and a record percentage of the population. The situation is dire, and of course has gotten worse during Obama’s term.

Partisan-wise, however, the wider graph shows that Food Stamp rolls increased by almost 11 million under President Bush, rising in all but one year of his presidency. So after the 2001 recession, unlike the previous two recessions, there was no major fall back in participation. By the end of Bill Clinton’s term, in contrast, Food Stamp numbers were back down below where they had been in the 1980s.

As a smaller point, in the narrow time range of the meme version graph, you can’t see that the number served has risen proportionately faster than the percentage served. In terms of the change shown on the graph it is noticeable: from the previous peak in 1994, the number served has increased by a multiple of 1.6, while the percentage served is up just 1.3-times.

Conclusion

With its simplistic storyline, welfare-state target, and official-source labeling, this graphic meme is well-adapted to its host environment: gullible, mean-spirited members of the blogosphere’s conservative echo chambers.

The simple blog post I have laid out here might be able to block the successful replication of the meme, but prospects for producing a delivery system capable of reaching the target are bleak in the absence of corporate funding for research and development. Like other promising cures, it may never see the clinical light of day.

Sheesh. I think I need a vacation.

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