by    in Data, Data Science, Popular Lists

Applying Machine Learning to the Diversity within our Worst Presidents List

Ranker visitors come from a diverse array of backgrounds, perspectives and opinions.  The diversity of the visitors, however, is often lost when we look at the overall rankings of the lists, due to the fact that the rankings reflect a raw average of all the votes on a given item–regardless of how voters behave on multiple other items.  It would be useful then, to figure out more about how users are voting across a range of items, and to recreate some of the diversity inherent in how people vote on the lists.

Take for instance, one of our most popular lists: Ranking the Worst U.S. Presidents, which has been voted on by over 60,000 people, and is comprised of over a half a million votes.

In this partisan age, it is easy to imagine that such a list would create some discord. So when we look at the average voting behavior of all the voters, the list itself has some inconsistencies.  For instance, the five worst-rated presidents alternate along party lines–which is unlikely to represent a historically accurate account of which presidents are actually the worst.  The result is a list that represents our partisan opinions about our nation’s presidents:

 

ListScreenShot

 

The list itself provides an interesting glimpse of what happens when two parties collide in voting for the worst presidents, but we are missing interesting data that can inform us about how diverse our visitors are.  So how can we reconstruct the diverse groups of voters on the list such that we can see how clusters of voters might be ranking the list?

To solve this, we turn to a common machine learning technique referred to as “k-means clustering.” K-means clustering takes the voting data for each user, summarizes it into a result, and then finds other users with similar voting patterns.  The k-means algorithm is not given any information whatsoever from me as the data scientist, and has no real idea what the data mean at all.  It is just looking at each Ranker visitor’s votes and looking for people who vote similarly, then clustering the patterns according to the data itself.  K-means can be done to parse as many clusters of data as you like, and there are ways to determine how many clusters should be used.  Once the clusters are drawn, I re-rank the presidents for each cluster using Ranker’s algorithm, and the we can see how different clusters ranked the presidents.

As it happens, there are some differences in how clusters of Ranker visitors voted on the list.  In a two-cluster analysis, we find two groups of people with almost completely opposite voting behavior.

(*Note that since this is a list of voting on the worst president, the rankings are not asking voters to rank the presidents from best to worst, it is more a ranking of how much worse each president is compared to the others)

The k-means analysis found one cluster that appears to think Republican presidents are worst:

ClusterOneB

Here is the other cluster, with opposite voting behavior:

ClusterTwoB

In this two-cluster analysis, the shape of the data is pretty clear, and fits our preconceived picture of how partisan politics might be voting on the list.  But there is a bias toward recent presidents, and the lists do not mimic academic lists and polls ranking the worst presidents.

To explore the data further, I used a five cluster analysis–in other words, looking for five different types of voters in the data.

Here is what the five cluster analysis returned:

FiveClusterRankings

The results show a little more diversity in how the clusters ranked the presidents.  Again, we see some clusters that are more or less voting along party lines based on recent presidents (Clusters 5 and 4).  Cluster 1 and 3 also are interesting in that the algorithm also seems to be picking up clusters of visitors who are voting for people that have not been president (Hillary Clinton, Ben Carson), and thankfully were never president (Adolf Hitler).  Cluster 2 and 3 are most interesting to me however, as they seem to show a greater resemblance to the academic lists of worst presidents, (for reference, see wikipedia’s rankings of presidents) but the clusters tend toward a more historical bent on how we think of these presidents–I think of this as a more informed partisan-ship.

By understanding the diverse sets of users that make up our crowdranked lists, we are able to improve our overall rankings, and also provide more nuanced understanding how different group opinions compare, beyond the demographic groups we currently expose on our Ultimate Lists.  Such analyses help us determine outliers and agenda pushers in the voting patterns, as well as allowing us to rebalance our sample to make lists that more closely resemble a national average.

  • Glenn Fox

 

 

by    in Data Science, Popular Lists, Rankings

In Good Company: Varieties of Women we would like to Drink With

They say you’re defined by the company you keep.  But how are you defined by the company you want to keep?

The list “Famous Women You’d Want to Have a Beer With”  provides an interesting way to examine this idea.  In other words, how people vote on this list can define something about what kind of person is doing the voting.

We can think of people as having many traits, or dimensions.  The traits and dimensions that are most important to the voters will be given higher rankings.  For instance, some people may rank the list thinking about the trait of how funny the person is, so may be more inclined to rate comedians higher than drama actresses.  Others may vote just on attractiveness, or based on singing talent, etc…  It may be the case that some people rank comedians and singers in a certain way, whereas others would only spend time with models and actresses.  By examining how people rank the various celebrities along these dimensions, we can learn something about the people doing the voting.

The rankings on the site, however, are based on the sum of all of the voters’ behavior on the list, so the final rankings do not tell us about how certain types of people are voting on the list.  While we could manually go through the list to sort the celebrities according to their traits, i.e. put comedians with comedians, singers with singers,  we would risk using our own biases to put voters into categories where they do not naturally belong.  It would be much better to let the voter’s own voting decide how the celebrities should be clustered.  To do this, we can use some fancy-math techniques from machine learning, called clustering algorithms, to let a computer examine the voting patterns and then tell us which patterns are similar between all the voters.   In other words, we use the algorithm to find patterns in the voting data, to then put similar patterns together into groups of voters, and then examine how the different groups of voters ranked the celebrities.  How each group ranked the celebrities tells us something about the group, and about the type of people they would like to keep them company.

As it happens, using this approach actually finds unique clusters, or groups, in the voting data, and we can then guess for ourselves how the voters from each group can be defined based on the company they wish to keep.

Here are the results:

Cluster 1:

Cluster4_MakeCelebPanels

Cluster 1 includes females known to be funny, and includes established comedians like Carol Burnett and Ellen DeGeneres. What is interesting is that Emma Stone and Jennifer Lawrence are also included, who are also highly ranked on lists based on physical attractiveness, they also have a reputation for being funny.  The clustering algorithm is showing us that they are often categorized alongside other funny females as well.  Among the clusters, this cluster has the highest proportion of female voters, which may explain why the celebrities are ranked along dimensions other than attractiveness.

 

Cluster 2:

Cluster1_MakeCelebPanels

Cluster 2 appears to consist of celebrities that are more in the nerdy camp, with Yvonne Strahovski and Morena Baccarin, both of whom play roles on shows popular with science fiction fans.  In the bottom of this list we see something of a contrarian streak as well, with downvotes handed out to some of the best known celebrities who rank highly on the list overall.

Cluster 3:

Cluster2_MakeCelebPanels

Cluster 3 is a bit more of a puzzle.  The celebrities tend to be a bit older, and come from a wide variety of backgrounds that are less known for a single role or attribute.  This cluster could be basing their votes more on the celebrity’s degree of uniqueness, which is somewhat in contrast with the bottom ranked celebrities who represent the most common and regularly listed female celebrities on Ranker.

Cluster 4:

Cluster3_MakeCelebPanels

We would also expect a list such as this to be heavily correlated with physical attractiveness, or perhaps for the celebrity’s role as a model.  Cluster 4 is perhaps the best example of this, and likely represents our youngest cluster.  The top ranked women are from the entertainment sector and are known for their looks, whereas in the bottom ranked people are from politics, comedy, or are older and probably less well known to the younger voters.  As we might expect, cluster 3 also has a high proportion of younger voters.

Here is the list of the top and bottom ten for each cluster (note that the order within these lists is not particularly important since the celebrity’s scores will be very close to one another):

TopCelebsPerClusterTable

 

In the end, the adage that we are defined by the company we keep appears to have some merit–and can be detected with machine learning approaches.  Though not a perfect split among the groups, there were trends in each group that drew the people of the cluster together.  This approach can provide a useful tool as we improve the site and improve the content for our visitors.   We are using these approaches to help improve the site and to provide better content to our visitors.

 

–Glenn R. Fox, PhD

 

 

A Ranker World of Comedy Opinion Graph: Who Connects the Funny Universe?

In the previous post, we showed how a Gephi layout algorithm was able to capture different domains in the world of comedy across all of the Ranker lists tagged with the word “funny”.  However, these algorithms also give us information about the roles that individuals play within clusters. The size of the node indicates that node’s ability to connect other nodes, so bigger nodes indicate individuals who serve as a gateway between different nodes and categories.  These are the nodes that you would want to target if you wanted to reach the broadest audience, as people who like these comedic individuals also like many others.  Sort of like having that one friend who knows everyone send out the event invite instead of having to send it to a smaller group of friends in your own social network and hoping it gets around. So who connects the comedic universe?

The short answer: Dave Chappelle (click to enlarge)

Chappelle

Dave Chappelle is the superconnector. He has both the largest number of direct connections and the largest number of overall connections. If you want to reach the most people, go to him. If you want to connect people between different kinds of comedy, go to him.  He is the center of the comedic universe. He’s not the only one with connections though.

Top 10 Overall Connectors

  1. Dave Chappelle 
  2. Eddie Izzard 
  3. John Cleese 
  4. Ricky Gervais
  5. Rowan Atkinson
  6. Eric Idle
  7. Billy Connolly
  8. Bill Hicks
  9. It’s Always Sunny In Philadelphia
  10. Sarah Silverman

 

We can also look at who the biggest connectors are between different comedy domains.

  • Contemporary TV Shows: It’s Always Sunny in Philadelphia, ALF, and The Daily Show are the strongest connectors. They provide bridges to all 6 other comedy domains.
  • Contemporary Comedians on American Television: Dave Chappelle, Eddie Izzard and Ricky Gervais are the strongest connectors. They provide bridges to all 6 other comedy domains.
  •  Classic Comedians: John Cleese and Eric Idle are the strongest connectors. They provide bridges to all 6 other comedy domains.
  • Classic TV Shows: The Muppet Show and Monty Python’s Flying Circus are the strongest connectors. They provide bridges to Classic TV Comedians, Animated TV shows, and Classic Comedy Movies.
  • British Comedians: Rowan Atkinson is the strongest connector. He serves as a bridge to all of the other 6 comedy domains.
  • Animated TV Shows: South Park is the strongest connector. It serves as a bridge to Classic Comedians, Classic TV Shows, and British Comedians.
  • Classic Comedy Movies: None of the nodes in this domain were strong connectors to other domains, though National Lampoon’s Christmas Vacation was the strongest node in this network.

 

 

A Ranker Opinion Graph of the Domains of the World of Comedy

One unique aspect of Ranker data is that people rank a wide variety of lists, allowing us to look at connections beyond the scope of any individual topic.  We compiled data from all of the lists on Ranker with the word “funny” to get a bigger picture of the interconnected world of comedy.  Using Gephi layout algorithms, we were able to create an Opinion Graph which categorizes comedy domains and identify points of intersection between them (click to make larger).

all3sm

In the following graphs, colors indicate different comedic categories that emerged from a cluster analysis, and the connecting lines indicate correlations between different nodes with thicker lines indicating stronger relationships.  Circles (or nodes) that are closest together are most similar.  The classification algorithm produced 7 comedy domains:

 

CurrentTVwAmerican TV Shows and Characters: 26% of comedy, central nodes =  It’s Always Sunny in Philadelphia, ALF, The Daily Show, Chappelle’s Show, and Friends.

NowComedianwContemporary Comedians on American Television: 25% of nodes, includes Dave Chappelle, Eddie Izzard, Ricky Gervais, Billy Connolly, and Bill Hicks.

 

ClassicComedianswClassic Comedians: 15% of comedy, central nodes = John Cleese, Eric Idle, Michael Palin, Charlie Chaplin, and George Carlin.

ClassicTVClassic TV Shows and Characters: 14% of comedy, central nodes = The Muppet Show, Monty Python’s Flying Circus, In Living Color, WKRP in Cincinnati, and The Carol Burnett Show.

BritComwBritish Comedians: 9% of comedy, central nodes = Rowan Atkinson, Jennifer Saunders, Stephen Fry, Hugh Laurie, and Dawn French.

AnimwAnimated TV Shows and Characters: 9% of comedy, central nodes = South Park, Family Guy, Futurama, The Simpsons, and Moe Szyslak.

MovieswClassic Comedy Movies: 1.5% of comedy, central nodes = National Lampoon’s Christmas Vacation, Ghostbusters, Airplane!, Vacation, and Caddyshack.

 

 

Clusters that are the most similar (most overlap/closest together):

  • Classic TV Shows and Contemporary TV Shows
  • British Comedians and Classic TV shows
  • British Comedians and Contemporary Comedians on American Television
  • Animated TV Shows and Contemporary TV Shows

Clusters that are the most distinct (lest overlap/furthest apart):

  • Classic Comedy Movies do not overlap with any other comedy domains
  • Animated TV Shows and British Comedians
  • Contemporary Comedians on American Television and Classic TV Shows

 

Take a look at our follow-up post on the individuals who connect the comedic universe.

– Kate Johnson

 

by    in Popular Lists

Dirty Airline Secrets, ’90s Supermodels + Not-So-Smart Criminals

Spring is in full swing and we can hear the birds chirping. Passover, Easter, Earth Day, 4/20… there sure is a lot to celebrate this month! Our gift to you: a handy roundup of our favorite new lists on Ranker.

9 Bad Things That Happened on Good Friday
Though it is not the merriest holiday for Christians (or Jesus for that matter), it still seems pretty crazy that so many murders, crimes, and disasters have gone down on Good Friday.

18 Dirty Facts About Flying That Airlines Don’t Want You To Know
Warning: some of these appalling airline practices cannot be unseen. If you’re squeamish, there is still time to turn back and continue to be blissfully unaware that your aircraft’s wing may or may not be held on with duct tape.

15 Criminals Who Got Caught By Bragging About Their Crimes
Some people are just in the habit of sharing everything about themselves on the Internet. You eat mac n’ cheese for dinner, you tweet a pic of yourself enjoying your meal. You rob a bank, you post a pic of yourself fanning out the money–D’oh!

Easy Things You Can Do Today to Help Your Environment
You don’t have to be a hero or a hippie to know that our environment could use a little help. In honor of Earth Day this month, here’s a fun roundup of easy things you can do to make a difference.

Stunning Snapshots of ’90s Supermodels (Then and Now)
Cindy Crawford, Claudia Schiffer, Naomi Campbell. . . they were all household names back in the ’90s. We all know that Tyra Banks is as crazy as ever, but what are the rest of these beauties up to and how have they aged?

20 Jobs That No Longer Exist
It’s insane that PEOPLE ever did some of these tasks. (See: the job that was replaced when alarm clocks were invented.) But they did and we have the pictures to prove it.

The Worst People On Planes
Whenever you board a plane, you hope that everything runs smoothly. Of course, that means safely, but it also means not having to deal with the many types of obnoxious people you’re likely to encounter. Here’s a rundown of the usual suspects.

by    in Trends

Bruno Mars Is Disliked, But Mostly by Older People

Bruno Mars With His Grammy

Bruno Mars is #98 out of the 468 worst bands of all time, as voted on by over 12.5k voters. But it turns out that older people dislike him way more than younger people.

Super Bowl XLVIII is upon us! Woop, woop! Time to prepare for head-spinning sensory overload: brawny men knocking each others’ lights out, sassy cheerleaders, flashy TV ads… it’s almost too much to handle. And what about the crown jewel of the day’s entertainment: the halftime performer, Bruno Mars?

Will Peter Gene Hernandez, aka Bruno Mars, have the charisma to command a crowd of 100,000 screaming fans, not to mention the 110 million Americans who are expected to watch the game from home?

According to our data, it’s not looking good. At least not on the surface. Bruno Mars is currently ranked #98 on our list of The Worst Bands of All Time.

As of right now, 12,627 people have voted on this list, which means that there are a whole lot of haters out there. Compound that with the fact that people always complain about the Super Bowl halftime performer, and it’s looking like Bruno Mars may not get a lot of love for his performance.


Puppy Bowl FTWLove for BeyonceStanding up for Amurika

However, when we slice the data up a little, it actually looks like things may not be that bad for the incredibly short crooner.

Why’s that, you ask?

1. Not to be taken lightly: Bruno Mars Has Got Some Serious Dance Moves.

Even though he was voted as one of the worst bands of all-time, people also acknowledge that he’s got some moves, which bodes well for him as a performer—especially in a situation where he is expected to wow the crowd. He was voted #29 out of 54 on this list of the best dancing singers.

Bruno Mars Dancing GIF
Bruno’s got moves.

2. Young people like Bruno Mars way more than this list would have you believe.  

Statistically Speaking: If we isolate the votes coming from only young people—people ages 30 and under, that is, Bruno Mars would drop all the way down to #381 on the list of worst bands of all time.

Only 5 out of 12 young people who voted on this list upvoted Bruno Mars. That’s about 40% who agree that he should be considered one of the worst bands of all time.

Compare that to say, Justin Bieber who received 16 upvotes for every 19 people who voted on him, which is close to 85%.

*Or, in Plain English if You’re Starting to Get a Headache: Being #381 on a ‘worst bands’ list is way better than being #1. Young people voting him down on this list means that a lot of them do not think that he is the worst.

3. Old people hating on Bruno Mars is making him rise in the ‘worst of’ rankings. 

Bruno Mars loses the mic GIF
What do you have to say about that, Bruno?

Let’s look at how old people (over the age of 50) feel about our buddy Bruno. If we strip out all of the young and middle-aged voters, Bruno Mars would climb up to #82 on the list of worst bands of all time. Remember, getting closer to #1 on a ‘worst’ list is not a good thing.

The ratio for this demographic is much higher. 3 out of 5 old-timers who voted on this list upvoted Bruno Mars as the worst band of all time. That’s 60% for those of you who are keeping track.

While we can crunch the numbers for preferences according to age, it must be noted that we do not have a specific reason as to why people voted this way. Why do people over 50 dislike Bruno Mars? Is it his cavalier attitude, his voluminous hair, his sexy lyrics? His widely-publicized cocaine bust?

Bruno Mars' mug shot actually isn't that bad.
Bruno Mars’ mug shot actually isn’t that bad.

Either way–we’d bet that Bruno would rather be pleasing young’uns than winning the hearts of old folks. They are the ones, after all, who will be more likely to pay to see him perform live (they’ll also be way more likely to pirate his music, but that’s another conversation).

So, while you are eating your triple beany cheese nachos and downing Bud heavies this weekend and one of your friends starts to complain about how much he hates Bruno Mars…you can think to yourself (or gently point out) that maybe he’s just too old to understand him.

Ranker Opinion Graph: the Best Froyo Toppings

Its hard to resist a cold treat on a hot summer afternoon, and frozen yogurt shops with their array of flavors and toppings have a little of something for everyone. Once you’re done agonizing over whether you want new york cheesecake or wild berry froyo (and trying a sample of each at least twice), its time for the topping bar. But which topping should you choose? We asked people to vote for their favorite frozen yogurt toppings on Ranker from a list of 32 toppings, and they responded with over 7,500 votes.

The Top 5 Frozen Yogurt Toppings (by number of upvotes):
1. Oreo (235 votes)
2. Strawberries(225 votes)
3. Brownie bits (223 votes)
4. Hot fudge (216 votes)
5. Whipped cream (201 votes)

But let’s be honest, who can just choose just ONE topping for their froyo? Using Gephi and data from Ranker’s Opinion Graph, we ran a cluster analysis on people’s favorite froyo topping votes to determine which toppings people like to eat together (click on graph to enlarge). In the graph, larger circles mean more likes with other toppings. Most of the versatile toppings were either a syrup (like strawberry sauce) or chocolate candy (like Reese’s Pieces).froyo

The 10 Most Versatile Froyo Toppings:

1. Strawberry sauce
2. Snickers
3. Magic Shell
4. White Chocolate chips
5. Peanut butter chips
6. Butterscotch syrup
7. Candies Nestle Butterfinger Bar
8. Reese’s Pieces
9. M&Ms
10. Brownie bits

 

Using the modularity clustering tool in Gephi, we were then able to sort toppings into groups based on which toppings people were most likely to upvote together. We identified 4 kinds of froyo topping lovers:

fruitnut1. Fruit and Nuts (Blue): This cluster is all about the fruits and nuts. These people love Strawberry sauce, sliced almonds, and Marschino cherries.

chocolate2. Chocolate (purple): This cluster encompases all things chocolate. These people love Magic Shell, Brownie bits, and chocolate syrup.

 

sugar3. Sugar candy (green): This cluster is made up of pure sugar. These people love gummy worms, Rainbow sprinkles, and Skittles.

 

 

salty4. Salty and Cake (Red): This cluster encompasses cake bites and toppings that have a salty taste to them. These people like Snickers, Cheesecake bits, and Caramel Syrup.

 

Some additional thoughts:

  • Banana was a strange topping that was only linked with Snickers.
  •  People who like nuts like both fruit and items from the salty category.
  •  People who like blueberries only like other fruits.
  • People who like sugar items like gummy worms also like chocolate, but don’t particularly like fruit.

 

– Kate Johnson

by    in New Features

New Features on Ranker

As usual, we are hard at work here in the Internet factory trying to make our site better and better. Pretty soon, Ranker.com is going to be able to walk your dog and drop your kids off at soccer practice. Those jet-pack days are not here yet, but we do have some other fun, new stuff for you to gaze upon. And maybe also use.

Do you sometimes see a list and think, “I want to rank that, but who has the TIME?” Not to fear, good people. We’ve actually reduced the amount of time it takes to register your opinion in list-form. With science!

Say you go to a list, any list. You start voting, all casual-like… and suddenly this tab pops out of the right side of your browser. Every time you vote something up, the number on the counter goes up, too! If you click on the green button that says “Your Votes,” a starter-list will come sliding in from the right side. You can literally re-order items, delete items, add items, write copy, and even add images and/or videos RIGHT THERE. And then publish your re-rank. RIGHT THERE. I mean, think of the time you just saved. Now you finally have time to cut your toenails. You’re welcome.

This isn’t technically new… but it’s something that looks new, so we’re counting it. The trigger buttons that switch how you view lists have moved! Not that exciting, I guess.

But in case you wondered why your ability to change ‘blog view’ to ‘info view’ or ‘info view’ to ‘slideshow’ wasn’t where it was supposed to be, just move your eyeballs to the left side of the screen and look right at the top of the list. There they are! Everything is still as it should be, only different.

We’ve gone and moved into the fine year of 2010 by finally making a functioning mobile version of our site. You can browse our lists more easily now, vote more easily and now you can actually re-rank a list… on your PHONE!

Unfortunately, we don’t yet support creating your own, brand-new lists on mobile, and there are some other user features of the site that we still won’t support on mobile… BUT it should be a lot easier to find, read, and vote on all your favorite lists.

Have you ever just wanted to copy and paste a list you see on Ranker? Maybe to a blog post, email, or just to Facebook? Well, now you can!

If you go into the ‘more options’ dropdown at the top of every list and select “Paste to Clipboard,” a popup with the list as it appears in text format will appear, allowing you to copy it and paste it wherever you want, free of formatting. If you want to paste it to Facebook, make sure to select the checkbox that says, “Check here to copy for Facebook.”

Enjoy. Check back soon for more fun new features!

by    in New Features

Latest Features on Ranker

There are a lot of neat little things we’ve been working on around here in the lab. Things that make it easier and more fun to make the lists you want to make. Take a peek:

Send A Note

We were sitting around the other day in the conference room and someone said ‘hey, wouldn’t it be cool if users could talk to each other? Like email, sorta?”

So we decided that you guys should totally get in on this whole “electronic” form of communication. Now, If you read a list you like, or are intrigued by the genius behind “5 Ways To Make Homemade Spam”, you can go to their profile page and send the list-maker a note and let them know that there are actually 6 ways! And, because we believe in the goodness of the human spirit, we are sure you guys won’t use this new power for evil.

Send A Note

PS. You need to be logged in to see this new feature!

 

Adding Items

Remember that time you made your favorite movie list? But you couldn’t remember ALL your favorite movies, because you’re not a damned robot, right? And then you were looking at someone else’s favorite movie list – or maybe perusing the Best Movies of All Time list – and you saw Piranha II: The Spawning listed there. That is totally one of your favorite movies, but you forgot until just now! Well, we have a way for you to add it to your own list with a single click. If you click that blue ‘+’ button, you will get a dropdown with any relevant lists of yours that Piranha II might be good to add to. Select your favorite movie list from the dropdown and POW, that James Cameron classic is now on your own list, too!

Adding Items

PS. You need to be logged in to see this new feature!

 

SlideShow View

You already know that you have two choices for how your list displays on Ranker. you can write lots of lovely words for the internet to read with big pictures… or you can just create easily digestable stacked lists with small images. Now we give you a third option… Slideshow! Build your list like normal in Edit, put in nice pretty images that will look good big — this view supports any commentary you might want to add, too! Choose the ‘slideshow view’ option from your ‘list options’ popup, and when you publish your list will display one beautiful item at a time!

SlideShow View

 

Filtering Lists

We have so many lists on Ranker. So. Many. And sometimes it’s overwhelming, we know. God, we know. But we’ve been tagging lists (and so have you) for the last few years and we finally went ahead and made use of them. Now, when you go into any of the big category tabs on ranker (film, tv, people, etc) you will see a little array of blue buttons on the top of the right sidebar. You can use these little buttons to sort and filter the content of that category in a million different ways! Each new filter button will narrow down your results until you find the exact lists you are looking for. Go try it!

Filtering Lists

 

Stylish Copy

One of the things we’ve never really had so much around here is the ability to dress up the things you guys are writing on your blog view lists. Bolding, italics, stuff like that. Well, fret no more! We now support a simple text styling interface in Edit.

When you are building your lists, and you want to write stuff… just click on the text field for your item. There is a whole little string of new tools there that allows you to make your text a lot fancier! And easy! Always easy!

Stylish Copy

by    in Opinion Graph

The Best Possible Answers To Opinion-Based Questions

Ranker, as an openended platform for ranking people/places/things, is a lot of different (awesome) things to different people. But the overarching goal for Ranker has always been to provide the best possible answer to opinion-based questions like “What are the best _____?”

Popular sports and entertainment vote lists often grow into being a great answer within 12-72 hours as they get lots of traffic quickly, but the majority of Ranker lists take 1 – 3 months to build to full credibility as visitors on Ranker and from search engines find them and shape them with votes and re-ranks.

I thought it would be fun to showcase some Ultimate Lists and Vote Lists in other categories that haven’t gone viral, but through the participation of lots of Rankers over a few months have indeed become “the best possible answer” to this question.

 

Food: you all clearly love to weigh in on the start of the day, and the 5 o’clock hour:

Best Breakfast Cereals

The Best Cocktails

But you also have strong opinions on hydration during the day:

Best Sodas (and for the more calorie-conscious among you The Best Diet Sodas)

And even specific Gatorade flavors (thanks for the list Lucas)

Snacking, whether it be on a particular type of cheese, candy bar, or even as granular as a specific Jelly Belly flavor (thanks for the list Samantha but what’s with all the chocolate pudding haters?)

Dining out, specifically at Italian chain restaurants

A list I am not authorized to vote on, pregnancy cravings

And hundreds more, including perhaps a new category entirely – food nostalgia (I do miss those Crispy M&Ms myself)

 

Fashion/Beauty: not categories that I personally check up on much, so I was psyched to see quite a few solid rankings here, some of them high-end but mostly stuff you can find at the mall:

Best women’s shoe brands

Best denim brands

Top handbag designers

Fashion Blogs

Sulfate-free shampoos

And even a men’s facial moisturizers list (have only tried 3 or 4 myself, but agree with their relative positions on the list)

 

Travel: Rankers, I know from a number of you that as we’ve been adding datasets of “rank-able objects” over the last year, one of the most-requested ones that we don’t yet have is hotels/resorts. Trust me, it’s still on the list. But in the meantime, it’s been heartening to see how many of you have participated in these great resources for travel destinations and attractions, like these:

Best US cities for vacations

Honeymoon destinations

Coolest cities in America

Theme parks for roller coaster addicts

And my personal faves, “bucket lists” of the world’s most beautiful natural wonders and historical landmarks.

Great stuff – these lists and 1000s more like them are true testimonials to the “wisdom of crowds”.  Thanks, crowds!

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