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 Data Science, prediction, Rankings

Cognitive Models for the Intelligent Aggregation of Lists

Ranker is constantly working to improve our crowdsourced list algorithms, in order to surface the best possible answers to the questions on our site.  As part of this effort, we work with leading academics who research the “wisdom of crowds”, and below is a poster we recently presented at the annual meeting for the Association for Psychological Science (led by Ravi Selker at the University of Amsterdam and in collaboration with Michael Lee from the University of California-Irvine).

While the math behind the aggregation model may be complex (a paper describing it in detail will hopefully be published shortly), the principle being demonstrated is relatively simple.  Specifically, aggregating lists using models that take into account the inferred expertise of the list maker outperform simple averages, when compared to real-world ground truths (e.g. box office revenue).  While Ranker’s algorithms for determining our crowdsourced rankings may be similarly complex, they are similarly designed to produce the best answers possible.

 

cognitive_model_aggregating_lists

 

– Ravi Iyer

by    in Data, Data Science, Opinion Graph

A Ranker Opinion Graph of Important Life Goals

What does it mean to be successful, and what life goals should we be setting in order to get there? Is spending time with family most important? What about your career?  We asked people to rank their life goals in order of importance on Ranker, and using a layout algorithm (force atlas in Gephi), we were able to determine goal categories and organized these goals into a layout which placed goals most closely related nearer to each other.

The connecting lines in the graph represent significant correlations or relationships between different life goals, with thicker lines indicating stronger relationships.  The colors in the graph differentiate between unique groups that emerged from a cluster analysis.  Click on the below graph to expand it.

all_black

The classification algorithm produced 5 main life goal clusters:
(1) Religion/Spirituality (e.g., Christian values, achieving Religion & Spirituality),
(2) Achievement and Material Goods (e.g., being a leader, avoiding failure, having money/wealth),
(3) Interpersonal Involvement/Moral Values (e.g., sharing life, doing the right thing, being inspiring),
(4) Personal Growth (e.g., achieving wisdom & serenity, pursuing ideals and passions, peace of mind), and
(5) Emotional/Physical Well-Being (e.g., being healthy, enjoying life, being happy).

These clusters are well matched to those identified by Robert Emmon’s (1999) psychological research on goal pursuit and well-being. Emmon’s found that life goals form 4 primary categories: work and achievement, relationships and intimacy, religion and spirituality, and generativity (leaving legacy/contributing to society).

However, not all goals are created equal.  While success related goals may be able to help us get ahead in life, they also have downsides.   People who focus on zero-sum goals such as work and achievement tend to report less happiness and life satisfaction compared to people who pursue goals. Our data also show a large divide between Well-being and Work/Achievement goals with relatively no overlap between these two groups.

Other interesting relationships in our graph:

  • Goals related to moral values (e.g., doing the right thing) were clustered with (and therefore more closely related to) interpersonal goals than they were to religious goals.
  • Sexuality was related to goals from opposite ends of the space in unique ways. Well-being goals were related to sexual intimacy whereas Achievement goals were related to promiscuity.
  • While most goal clusters were primarily made up of goals for pursuing positive outcomes, the Achievement/Material Goods goal cluster also included the most goals related to avoiding negative consequences (e.g., avoiding failure, avoiding effort, never going to jail).
  • Our Personal Growth goal cluster is unique from many of the traditional goal taxonomies in the psychological literature, and our data did not find the typical goal cluster related to Generativity. This may show a shift in goal striving from community growth to personal growth.

– Kate Johnson

Citation: Emmons, R. A. (1999). The psychology of ultimate concerns: Motivation and spirituality in personality. New York: Guilford Press.

 

by    in Opinion Graph, Ranker Comics

A Cluster Analysis of the Superpower Opinion Graph produces 5 Superhero types

If you could have one superpower, which would you choose?  Data from the Ranker list “Badass Superpowers We’d Give Anything to Have” improves on the age-old classroom ice breaker question by letting people rank all of the superpowers in order of how much they would want them.  Because really, unless you’re one of the X-men, you probably would have more than one power. So, if you could have a collection of superpowers, what kind of superhero would you be?

Using Gephi and data from Ranker’s Opinion Graph, we ran a cluster analysis on people’s votes on the superpowers list to determine what groupings of superpowers different people wanted.

This analysis grouped superpowers into 5 clusters, which we interpreted to represent unique superhero types.

 

The Overall Superpower Opinion Graph

Allpowers

 

 

The 5 Types of Superheroes

    god

1. The Creationist God: This superhero type is characterized by creation and destruction, Old-Testament Christian God-style. Notable superpowers: the ability to create/destroy worlds, die and come back to life, have gods’ weapons (Thor’s Hammer, Zeus’ Thunderbolt), remove others’ senses, and resurrect the dead.

timelord

2. The Time Lord: This superhero type is basically The Doctor from Dr. Who. Notable superpowers: omnipotence, travel to other dimensions, open portals to anywhere, and travel beyond the omniverse.

elementalist

3. The Elementalist: This superhero type has the ability to manipulate the elements and use them as weapons to their advantage. Notable superpowers: manipulation of water, fire, weather, and plants, ability to shapeshift, shoot ice, and lightning and fire.

superman

4. The Superhuman: This superhero type is humans+, with enhanced human senses and decreased human limitations. Notable superpowers: sense danger, x-ray vision, walk through walls, super speed, mind reading, flight, super strength, and enhanced flexibility.

zen

5. The Zen Master: This superhero type sounds a bit like being permanently on mind-altering psychoactive substances crossed with Gandhi. Notable superpowers: speech empowerment, spiritual enlightenment, and infinite appetite!!.

 

-Kate Johnson

by    in About Ranker, Opinion Graph, Pop Culture, Rankings

Ranker’s Rankings API Now in Beta

Increasingly, people are looking for specific answers to questions as opposed to webpages that happen to match the text they type into a search engine.  For example, if you search for the capital of France or the birthdate of Leonardo Da Vinci, you get a specific answer.  However, the questions that people ask are increasingly about opinions, not facts, as people are understandably more interested in what the best movie of 2013 was, as opposed to who the producer for Star Trek: Into Darkness was.

Enter Ranker’s Rankings API, which is currently now in beta, as we’d love the input of potential users’ of our API to help improve it.  Our API returns aggregated opinions about specific movies, people, tv shows, places, etc.  As an input, we can take a Wikipedia, Freebase, or Ranker ID.  For example, below is a request for information about Tom Cruise, using his Ranker ID from his Ranker page (contact us if you want to use other IDs to access).
http://api.ranker.com/rankings/?ids=2257588&type=RANKER

In the response to this request, you’ll get a set of Rankings for the requested object, including a set of list names (e.g. “listName”:”The Greatest 80s Teen Stars”), list urls (e.g. “listUrl”:”http://www.ranker.com/crowdranked-list/45-greatest-80_s-teen-stars” – note that the domain, www.ranker.com, is implied), item names (e.g. “itemName”:”Tom Cruise”) position of the item on this list (e.g. “position”:21), number of items on the list (e.g. “numItemsOnList”:70), the number of people who have voted on this list (e.g. “numVoters”:1149), the number of positive votes for this item (e.g. “numUpVotes”:245) vs. the number of negative votes (e.g. “numDownVotes”:169), and the Ranker list id (e.g. “listId”:584305).  Note that results are cached so they may not match the current page exactly.

Here is a snipped of the response for Tom Cruise.

[ { “itemName” : “Tom Cruise”,
“listId” : 346881,
“listName” : “The Greatest Film Actors & Actresses of All Time”,
“listUrl” : “http://www.ranker.com/crowdranked-list/the-greatest-film-actors-and-actresses-of-all-time”,
“numDownVotes” : 306,
“numItemsOnList” : 524,
“numUpVotes” : 285,
“numVoters” : 5305,
“position” : 85
},
{ “itemName” : “Tom Cruise”,
“listId” : 542455,
“listName” : “The Hottest Male Celebrities”,
“listUrl” : “http://www.ranker.com/crowdranked-list/hottest-male-celebrities”,
“numDownVotes” : 175,
“numItemsOnList” : 171,
“numUpVotes” : 86,
“numVoters” : 1937,
“position” : 63
},
{ “itemName” : “Tom Cruise”,
“listId” : 679173,
“listName” : “The Best Actors in Film History”,
“listUrl” : “http://www.ranker.com/crowdranked-list/best-actors”,
“numDownVotes” : 151,
“numItemsOnList” : 272,
“numUpVotes” : 124,
“numVoters” : 1507,
“position” : 102
}

…CLIPPED….
]

What can you do with this API?  Consider this page about Tom Cruise from Google’s Knowledge Graph.  It tells you his children, his spouse(s), and his movies.  But our API will tell you that he is one of the hottest male celebrities, an annoying A-List actor, an action star, a short actor, and an 80s teen star.  His name comes up in discussions of great actors, but he tends to get more downvotes than upvotes on such lists, and even shows up on lists of “overrated” actors.

We can provide this information, not just about actors, but also about politicians, books, places, movies, tv shows, bands, athletes, colleges, brands, food, beer, and more.  We will tend to have more information about entertainment related categories, for now, but as the domains of our lists grow, so too will the breadth of opinion related information available from our API.

Our API is free and no registration is required, though we would request that you provide links and attributions to the Ranker lists that provide this data.  We likely will add some free registration at some point.  There are currently no formal rate limits, though there are obviously practical limits so please contact us if you plan to use the API heavily as we may need to make changes to accommodate such usage.  Please do let me know (ravi a t ranker) your experiences with our API and any suggestions for improvements as we are definitely looking to improve upon our beta offering.

– Ravi Iyer

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 Data Science

Mitt Romney Should Have Advertised on the X-Files

With the election recently behind us, many political analysts are conducting analyses of the campaigns, examining what worked and what didn’t.  One specific area where the Obama team is getting praise is in their unprecedented use of data to drive campaign decisions, and even more specifically, how they used data to micro-target fans who watched specific TV shows.  From this New York Times article concerning the Obama Team’s TV analytics:

“Culling never-before-used data about viewing habits, and combining it with more personal information about the voters the campaign was trying to reach and persuade than was ever before available, the system allowed Mr. Obama’s team to direct advertising with a previously unheard-of level of efficiency, strategists from both sides agree….

[They] created a new set of ratings based on the political leanings of categories of people the Obama campaign was interested in reaching, allowing the campaign to buy its advertising on political terms as opposed to traditional television industry terms…..

[They focused] on niche networks and programs that did not necessarily deliver large audiences but, as Mr. Grisolano put it, did provide the right ones.”

 

The Obama team focused more on undecided/apolitical voters in an effort to get them to the polls.  Given that some Mitt Romney supporters have blamed a lack of turnout of supporters for the results of the election, perhaps Romney would have been smart to have created a ranked list of TV shows, based on how much fans of the shows supported Romney, and then placed positive/motivating ads on those shows in an effort to increase turnout of his base.  Where would Romney get such data?  From Ranker!

Mitt Romney is on many votable Ranker lists (e.g. Most Influential People of 2012) and based on people who voted on those lists and also lists such as our Best Recent TV Shows list, we can examine which TV shows are positively or negatively associated with Mitt Romney.  Below are the top positive results from one of our internal tools.

As you can see, the X-Files appears to be the highest correlated show, by a fair margin.  I don’t watch the X-Files, so I wasn’t sure why this correlation exists, but I did a bit of research, and found this article exploring how the X-Files supported a number of conservative themes, such as the persistence of evil, objective truth, and distrust of government (also see here).  The article points out that in one episode, right wing militiamen are depicted as being heroic, which never would happen in a more liberal leaning plot.  Perhaps if you are a conservative politician seeking to motivate your base, you should consider running ads on reruns of the X-Files, or if you run a television station that shows X-Files reruns, consider contacting your local conservative politicians leveraging this data.

You may notice that this list contains more classic/rerun shows (e.g. Leave it to Beaver) than current shows.  This appears to be part of a general trend where conservatives on Ranker tend to positively vote for classic TV, a subject we’ll cover in a future blog post.  The possibility of advertising on reruns is part of what we would like to highlight in this post, as ads are likely relatively cheap and audiences can be more easily targeted, a tactic which the Obama campaign has been praised for.  At Ranker, we’re hopeful that more advertisers will seek value in the long-tail and mid-tail and will seek to mimic the tactics of the Obama campaign, as our data is uniquely suited for such psychographic targeting.

– Ravi Iyer

by    in Market Research

On Taste Graphs and “Rushmore”

In a previous post, we talked about a bit about how Ranker collects users into like-minded “clusters” that allowed for statistical analysis. This method is how we were able to look at “Game of Thrones” fans and figure out other shows, characters, games and movies they might like.

Now, let’s dig a bit deeper into how this analysis works, and what sort of things we can learn from it. Essentially, breaking down the users who vote on our lists into clusters of people with similar taste lets us predict how fans of one thing will feel about some other thing.

We use the advertising term “Lift %” to represent this idea, but it basically boils down to an odds ratio. We’re measuring the projected increase in someone’s interest level for something, based on their preference for something else. Therefore, we don’t even just have to compare fans of one show to another, or fans of one movie to another. Sure, we can tell what TV shows you’ll probably like if you like “Game of Thrones,” but we can also tell what people you’ll respond to positively, or what websites you prefer, or your favorite athlete.

For another example, let’s look at the 1998 comedy-drama “Rushmore.” Along with “Bottle Rocket,” this was really the film that made Wes Anderson a household name, and also contains one of Bill Murray’s most beloved and iconic performances.

“Rushmore” appears on a number of Ranker lists (it’s rated as one of the Best High School Films of All Time AND one of the Best Serious Films Starring Comedians.) So we’ve managed to create a “cluster” of users who have voted “Rushmore” up on these lists, and who also seem to share some strong opinions about other topics in our system.

The first big trend we noticed among this like-minded cluster of “Rushmore” fans was that they tended to like other comedy films, too. Which you’d sort of expect. Except these fans tended to prefer classic comedies to more contemporary films. In fact, all of these films had a greater “Lift %” among “Rushmore” fans than any films made in the 1990s, when the film actually came out:

“Dr. Strangelove” (1964)
“The General” (1926)
“Modern Times” (1936)
“The Lady Eve” (1941)
“A Night at the Opera” (1935)

As well, all of these films had a Lift % of OVER 500%, which means someone who likes “Rushmore” is 500% more likely to enjoy, say, “A Night at the Opera,” than someone who is ambivalent about “Rushmore.” That strikes us as statistically significant. (The numbers are even higher the further up the list you go. A “Rushmore” fan is 1000% more likely to enjoy “Dr. Strangelove” than a random person.)

From what we can tell, it works the other way, too. “Rushmore” is the most popular overall film among “Annie Hall” fans and #4 overall among fans of Charlie Chaplin’s “City Lights.” Exactly WHY Wes Anderson’s coming-of-age dramedy scores so well among lovers of old movies is up for debate, but the correlation itself is not, really, based on the numbers.

We’re continuing to develop and fine-tune our reports, of course. And it’s worth remembering that we get the BEST results on popular stuff that gets voted on all the time. It’s not too hard to tell what kind of music Jay-Z fans will like (though we’ll save that for another blog post), but we won’t do nearly as well for Captain Beefheart fans. Yet.

– Lon

by    in About Ranker, Market Research

Game of Thrones Fan Report: Behind the Numbers

Last week, we published an info graphic with lots of “taste data” about “Game of Thrones” fans. Basically, we used all the data we’re collecting about people’s preferences in Ranker to make some educated guesses about what else people who like “Game of Thrones” might like. Why? Mostly because we can, but also because we figured people could potentially find it interesting.

After we showed the infographic to the world, a lot of people wrote to us asking how we actually arrived at these conclusions. (And yes, some of them just wanted to be sure we weren’t just making the whole thing up.)

It all starts with votes. Thousands of people have voted on Ranker lists on which “Game of Thrones” appears. If they’re on a list that’s “positive” (for example, “Best Premium Cable Shows”) and they vote “Game of Thrones” up, we know they like the show. If we notice they also vote for “Game of Thrones” on other lists (“Most Loving Caresses of Dragon Eggs in TV History,” for example), we know they REALLY like the show.

Then we look at all the other Ranker lists where that person has voted, and get a sense for what else they like, and what else they hate.

But we don’t stop there. The next step is to arrange people into clusters based on their specific preferences. If 80% of the people who vote on Ranker lists like “The Simpsons,” and 80% of “Game of Thrones” fans like “The Simpsons,” that’s not very meaningful at all. But if only 20% of people who vote like “The Simpsons,” and 80% of “Game of Thrones” fans like “The Simpsons,” then we’ve learned something statistically significant about these people.


But what about fans of “Simpsons” parodies of “Game of Thrones,” you might ask… if you were purposefully trying to confuse me.

These “clusters” of people with tastes that are aligned will teach us basically everything we need to know to make educated guesses about what random Ranker users will like. In our next post, we’ll explore exactly how we use these “taste clusters” to draw conclusions.

-Lon

by    in Market Research

Game of Thrones: The Fan Report

At Ranker HQ, we’re constantly monitoring the topics that get ranked a lot. It’s pretty easy to tell when a certain book or movie or musical artist is getting popular or hitting critical mass just based on how frequently the name is mentioned on lists. This is especially true of TV, where the start of a new season for a popular show means an eruption of lists mentioning that show. (Don’t believe me? Check out all the “Mad Men” lists streaming in!)

We weren’t necessarily surprised that HBO viewers were losing their heads for “Game of Thrones.” (See what I did there?) It’s back for Season 2, and obviously Rankers are going to have fun making tons of lists about the sword-and-sorcery-and-skin fantasy series based on George R. R. Martin’s novels. Instead, we were intrigued because the data reveals Game of Thrones fans are just as… idiosyncratic as the show they love. (Yes, idiosyncratic is a nice way of putting it. But hey, we’re not here to INSULT our users.)

And we say this not just because they watch a show in which incest happens as often as other series take commercial breaks. Also because they overwhelming love villainous characters and anti-heroes and they prefer a lot of lesser-known shows that failed to ever find an audience.

Read on for more insight into the weird, even twisted world of “Game of Thrones” fans (or Throne-heads, as we’ve dubbed them.)

Click for a larger version

Like the graphic? Feel free to repost it anywhere you like. Spread the word throughout the Seven Kingdoms!

-Lon

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