Category Archives: Research

Fun Trolling Facebook Polls (For Science (Actually Lulz)!)

I saw a Facebook Poll late last night that a friend had voted on. The question was something like “Which pair of shoes should I get?” The poll had links as answers, so the idea was that people look at the pics and let the guy decide which pair of shoes was better.

Apparently in Facebook Polls, you click on the answer to vote. And there’s no unvote (you can vote for another choice, but you can’t abstain after clicking). So people ended up clicking on the twitpic link thinking they’d see the image, and ended up accidentally voting on the poll. I fell for this, too. There were something like a couple thousand answers on that poll. I believe it’s been removed now.

I figured I could do better with a more salient question, so I made one up myself. “Which pair of glasses look better on me?” I made the question have two twitpic links, which you can view here and here if you actually copy and paste them in. I figured people are naturally judgers, and something like helping someone choose glasses to wear is an easy task (plus you theoretically get to see pictures of faces, which people just love, consciously or subconsciously).

I started the poll late last night, which probably didn’t help, but a few friends took the bait. I hope they forgive me as I did this for science the lulz! When I woke up this morning, there were currently 51 votes, from people I know, friends of friends, and even people two degrees out of my social network! I think it would be really interesting to see how this poll spreads through Facebook (assuming they don’t shut it down first).

I guess now that this post is published, any scientific value is gone (since you could be reading from anywhere and vote for my poll non-virally). The main point is that when you design systems very rigidly (in Facebook’s case, not letting people abstain from a poll, which believe it or not is a valid bit of information), interesting consequences pop up.

I’ll keep checking the status of the poll and see if it actually blows up, whimpers and dies or gets taken down quickly.

Analysis Edit:
I think another reason that this poll is so effective is that it makes it seem that the person who voted is the originator of the poll. Check out the newsfeed formatting:

The voter’s name is prominently displayed (though I blurred it) and the person who asked the question is nowhere to be seen.

Edit #1: The time is now about 12:40PM and the total number of voters has doubled to 99!

Edit #2: It’s about 1:10PM and the number has doubled again to 201!

Edit #3: The time is around 1:24PM and there’s 304 answers.

Edit #4: Alright, it’s 1:35PM and there’s 406 votes.
Edit #5: Wow. It’s 1:41PM and there’s 502 votes.
Edit #5: It’s 1:48 and there are 621 votes.
Edit #6: I’m just going to simplify my updates now…
1:53PM – 716 votes
1:58PM – 811 votes
2:02PM – 904 votes
2:07PM – 1031 votes
2:16PM – 1282 votes
2:22PM – 1442 votes
2:27PM – 1619 votes
2:38PM – 2013 votes
2:46PM – 2393 votes
2:50pm – 2604 votes
2:54pm – 2811 votes
2:58pm – 3038 votes
3:04pm – 3408 votes
3:11pm – 3861 votes
3:14pm – 4142 votes
3:23pm – 4761 votes
3:39pm – 6169 votes
3:47pm – 6806 votes
3:51pm – 7198 votes
3:56pm – 7693 votes
4:00pm – 8010 votes
4:06pm – 8624 votes
4:10pm – 9038 votes
4:19pm – 10,013 votes!
4:28pm – 11,007 votes
4:37pm – 12,009 votes
4:46pm – 13,046 votes
4:53pm – 14,009 votes
5:04pm – 15,216 votes
5:09pm – 15,886 votes (dinnertime)
5:45pm – 19,764 votes
5:55pm – 20,722 votes
6:06pm – 21,829 votes
6:30pm – 24,104 votes
6:40pm – 25,013 votes
6:51pm – 26,001 votes
7:02pm – 27,014 votes
7:14pm – 28,013 votes
7:26pm – 29,001 votes
7:42pm – 30,373 votes
7:53pm – 31,124 votes
(mini break)
9:41pm – 38,332 votes
10:14pm – 40,175 votes
10:34pm – 41,360 votes
10:50pm – 42,232 votes
11:38pm – 44,690 votes
12:12am – 46,761 votes
12:51am – 47,677 votes
1:48am – 49,358 votes
Day 2
10:10am – 53,601 votes
10:31am – 53,812 votes
12:48pm – 55,418 votes
1:07pm – 55,598
1:36pm – 55,923
2:32pm – 56,470
4:41pm – 57,559
10:36pm – 59,078
1:51am – 59,426
EDIT: Facebook finally deleted the poll, with something like 60,000 votes last time I checked.

Facebook Lexicon: Kinda Fun, But Useful?

Facebook just recently released a fun new thingy called Lexicon. It shows you a graph of the occurrences of certain words or phrases over time on all Facebook walls. Since I do quite a lot of research using Facebook, I thought I’d take a look at it.

Facebook has some suggested phrase pairs that you can use such as “party tonight” and “hungover.” And as expected, the phrases are cyclical and you tend to see a spike in “hungover” the day after “party tonight.” But it’s worth noting that you can’t prove causation by just correlation.

What’s a little strange is that there doesn’t seem to be any growth going on in Facebook. Maybe they’ve normalized the data so you can spot trends, but you’d kind of assume that word counts would trend towards going up since Facebook is still supposedly growing. Or is Lexicon telling us something Facebook doesn’t want us to know about their growth?

Anyway, at least one question of the ages has been put to rest. According to Lexicon, shampoo really is better than conditioner. STOP LOOKING AT ME, SWAN!

Bonus Analysis:

It looks like the word “happy” is used more on holidays like New Year’s Day and Valentine’s Day. But the word “merry” is kind of exclusively used on Christmas. See that strangely unhappy day near the beginning of March? Since it’s a leap year, Feb 29th was included in this graph. And since less people statistically have birthdays on that leap day, the word “happy” was recorded less. Pretty neat stuff, huh? I’m sure there’s other fun stuff to dig out of Lexicon, if you were really inclined to.

A Tale of Two Facebook Apps: Viral Vs. Non-Viral Growth

For my SI 508 Networks class last semester I did an analysis of one of my Facebook applications, Notecentric. Notecentric was a social network that I had written during the Summer of ’06 and I had recently ported it to the Facebook Developer Platform in Summer ’07 shortly after the platform had been launched.

The growth of Notecentric isn’t what I had hoped it would be. Not too many people use it, probably due to network effects of Facebook promoting a competing app (note to Facebook: if you want to promote a level playing field, don’t play favorites!) and other general performance issues (the application is pretty barebones and the RFacebook library I used to write it is pretty damn slow. It times out a lot!).

Anyway, I got some neat network data from it, which made the whole thing worthwhile. You can check out the original paper I wrote last semester here.

I’m going to be presenting my analysis during the School of Information’s annual expoSItion. It’s like a science fair except without the exploding volcanoes. During my Winter break, I developed another app, mainly for fun. It’s called Musical Instruments. Basically it lets you list which instruments you play and see which instruments your friends play. I had somewhat higher hopes for this application as a data gathering tool, and sure enough, it seems to be doing some cool stuff on first analysis. For expoSItion I figured I’d grab some data from this new app and compare the two.

I ran some initial analysis on the Musical Instruments app. I won’t go over a lot of the original metrics I used (number of peers with app installed, percentage of peers with app installed, etc) and I’ll just skip to the pictures.

This is an initial view of the Notecentric network:


Continue reading A Tale of Two Facebook Apps: Viral Vs. Non-Viral Growth

Google Developers Day US – Theorizing from Data

So before I got on a plane to fly from NM to RH, I prepared myself for boredom. One of the things I did was encode some of the Google Developer Day videos on Youtube for my Sony PSP. I think it was one of three times I’ve actually used my PSP!

Anyway, most of the videos were pretty fluffy and didn’t hold my interest, but the talk by Peter Norvig about statistical analysis was pretty darn interesting. Funny sidenote: when I went to Google for an interview, Peter Norvig was the special speaker dude. He had a pretty cool Hawaiian shirt on then too, as I recall.

Anyway, the talk brings up some pretty interesting things, like how if you feed enough statistics to a computer, the actual algorithm matters less and less. I’ve been interested in AI and machine learning, but I never really took any formal classes.

The stuff in the talk has sort of stuck in my head now. So I’m tending to see a lot of problems as being solvable by statistical analysis/classification. Like that Spock Challenge thing I blogged about earlier. Anyway, I’ve got an idea for a wacky application of Naive Bayesian Classification, but I won’t mention it yet (in case it’s an actual good idea, or in case I decide to bail after I don’t want to figure out the probability math).

Stay tuned?