by   Ranker
in Popular Lists

Things You Should Never Do While Naked, 90s Slang, Embarrassing Selfies + More

January is almost over. Good riddance! Have you given up on your New Year’s Resolutions yet? Trust us, you’ll feel much better once you just let go. For your enjoyment, here are the most popular lists that people have been upvoting on Ranker this month. Enjoy!

Incredible 90s Slang That We (Almost) Forgot About. Almost.

If you grew up in the ’90s, odds are that in between playing Pogs or watching reruns of “Saved by the Bell”, you were telling your mom to “talk to the hand” and that her cooking was Da Bomb…Not! Looking back, slang from the ’90s involved giving people a lot of attitude and tricking them.

The Last Words of 15 Famous Serial Killers

“I did not get my Spaghetti O’s. I got spaghetti. I want the press to know this.”

The Most Egregious Celebrity Wardrobe Malfunctions of 2013

Nip slips, wedgies and rips, oh my! Last year was an epic one for crazy celebrity wardrobe malfunctions. For your enjoyment, here are the best (read: most embarrassing).

The Most Extreme Body Transformations Ever Done for a Movie Role

Matthew McConaughey lost 47 pounds for his role in Dallas Buyer’s Club. Jared Leto lost nearly 40. Christian Bale packed on 43 pounds and a huge beer belly for American Hustle. Those aren’t even the most extreme cases! See the shocking before and after pictures of actors who completely changed their bodies for a movie role.

15 Reasons Why You Are the Most Annoying Person on Facebook

You instantly thought of at least one person when you saw the name of this list, didn’t you? Odds are that you have at least one special person in your life that is a major Facebook offender. Take heart, it happens to the best of us.

The Most Embarrassing Celebrity Selfies

Embarrassing selfies happen when a sexy guy or gal is trying just a little too hard to look good for the camera. They are obviously sucking it in (Chris Pratt, Justin Bieber) or showing a bit too much skin that no one wants to see (Lindsay Lohan). We would feel bad…but these celebs did post these photos to their own social media.

23 Things You Should Never Do While Naked

Similar to getting drunk and singing at the top of your lungs, being naked is fun (!!) if not always appropriate. Whether an activity involves sharp, flying objects, extreme heat or compromising positions, there are some things that you should just never do naked. Ever.

That’s it! Stay in touch and we hope you’re having a great month!

by   Ranker
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.

by   Ranker
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).

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”:”” – note that the domain,, 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” : “”,
“numDownVotes” : 306,
“numItemsOnList” : 524,
“numUpVotes” : 285,
“numVoters” : 5305,
“position” : 85
{ “itemName” : “Tom Cruise”,
“listId” : 542455,
“listName” : “The Hottest Male Celebrities”,
“listUrl” : “”,
“numDownVotes” : 175,
“numItemsOnList” : 171,
“numUpVotes” : 86,
“numVoters” : 1937,
“position” : 63
{ “itemName” : “Tom Cruise”,
“listId” : 679173,
“listName” : “The Best Actors in Film History”,
“listUrl” : “”,
“numDownVotes” : 151,
“numItemsOnList” : 272,
“numUpVotes” : 124,
“numVoters” : 1507,
“position” : 102


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

by   Ranker
in Data, Market Research, Opinion Graph, Popular Lists, Rankings

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

How Netflix’s AltGenre Movie Grammar Illustrates the Future of Search Personalization

I recently got sent this Atlantic article on how Netflix reverse engineered Hollywood by a few contacts, and it happens to mirror my long term vision for how Ranker’s data fits into the future of search personalization.  Netflix’s goal, to put “the right title in front of the right person at the right time,” is very similar to what Apple, Bing, Google, and Facebook are attempting to do with regards to personalized contextual search.  Rather than you having to type in “best kitchen gadgets for mothers”, applications like Google Now and Cue (bought by Apple) hope to eventually be able to surface this information to you in real time, knowing not only when your mother’s birthday is, but also that you tend to buy kitchen gadgets for her, and knowing what the best rated kitchen gadgets that aren’t too complex and are in your price range happen to be.  If the application was good enough, a lot of us would trust it to simply charge our credit card and send the right gift.  But obviously we are a long way from that reality.

Netflix’s altgenre movie grammar (e.g. Irreverent Werewolf Movies Of The 1960s) gives us a glimpse of the level of specificity that would be required to get us there.  Consider what you need to know to buy the right gift for your mom.  You aren’t just looking for a kitchen gadget, but one with specific attributes.  In altgenre terminology, you might be looking for “best simple, beautifully designed kitchen gadgets of 2014 that cost between $25 and $100” or “best kitchen gadgets for vegetarian technophobes”.  Google knows that simple text matching is not going to get it the level of precision necessary to provide such answers, which is why semantic search, where the precise meaning of pages is mapped, has become a strategic priority.

However, the universe of altgenre equivalents in the non-movie world is nearly endless (e.g. Netflix has thousands of ways just to classify movies), which is where Ranker comes in, as one of the world’s largest sources for collecting explicit cross-domain altgenre-like opinions.  Semantic data from sources like wikipedia, dbpedia, and freebase can help you put together factual altgenres like “of the 60s” or “that starred Brad Pitt“, but you need opinion ratings to put together subtler data like “guilty pleasures” or “toughest movie badasses“.  Netflix’s success is proof of the power of this level of specificity in personalizing movies and consider how they produced this knowledge.  Not through running machine learning algorithms on their endless stream of user behavior data, but rather by soliciting explicit ratings along these dimensions by paying “people to watch films and tag them with all kinds of metadata” using a “36-page training document that teaches them how to rate movies on their suggestive content, goriness, romance levels, and even narrative elements like plot conclusiveness.”  Some people may think that with enough data, TripAdvisor should be able to tell you which cities are “cool”, but big data is not always better data.  Most data scientists will tell you the importance of defining the features in any recommendation task (see this article for technical detail on this), rather than assuming that a large amount of data will reveal all of the right dimensions.  The wrong level of abstraction can make prediction akin to trying to predict who will win the superbowl by knowing the precise position and status of every cell in every player on every NFL team.  Netflix’s system allows them to make predictions at the right level of abstraction.

The future of search needs a Netflix grammar that goes beyond movies.  It needs to able to understand not only which movies are dark versus gritty, but also which cities are better babymoon destinations versus party cities and which rock singers are great vocalists versus great frontmen.  Ranker lists actually have a similar grammar to Netflix movies, except that we apply this grammar beyond the movie domain.  In a subsequent post, I’ll go into more detail about this, but suffice it to say for now that I’m hopeful that our data will eventually play a similar role in the personalization of non-movie content that Netflix’s microtagging plays in film recommendations.

– Ravi Iyer


by   Ranker
in New Features

Ranker's Sexy New Look for 2014

Ranky-Up-VoteOh, hey there! You may have noticed that we got a whole new look for 2014. We’ve built some cool new features and streamlined the look of our editing interface. Making lists on Ranker has never been easier.

Here’s what’s new:

  • New YES/NO switches make it a breeze to customize your list.


  • Searching and finding stuff is now 10x faster.


  • Reranking is easier than ever thanks to our improved list suggestion tool.


  • Now you can create a new list or add items to other peoples’ lists on the go. Hello, mobile! Imagine creating your grocery list on your phone, allowing your roommates to vote and then buying only the most popular items for the party you’re throwing that weekend. Wow, you sure are nice… are you looking for new roommates?

Take a look around, make a new list or two, and drop us a line if you have any feedback. We’d love to hear what you think. Oh, and happy new year!

by   Ranker
in Popular Lists

Bad Santas, Awkward Christmas Photos, and Celebs Who Have Killed

Happy (non-denominational) holidays to you all! As our gift to you, we present the very best lists that Ranker users have been upvoting this season. Enjoy!

Top Crimes Committed By Guys in Santa Suits

‘Tis the season to be jolly, greedy, lecherous and absolutely, undeniably unfit to set foot in public. In honor of the holiday season, here are thirteen crimes committed by guys in Santa suits: from bank robbers to mall flashers and child molesters. Merry Christmas.

The 20 Most Awkwardly Hilarious Family Christmas Photos

The fact that an entire family can gather together to take one photo is already a miracle, but the level of awkwardness captured on film here is absolutely brilliant.

The Best Christmas Songs Written by Jewish Songwriters

Here’s proof that a truly great songwriter can write about anything…including music celebrating a completely different faith and an imaginary elderly man who brings gifts to other people’s children.

29 Celebrities Who Have Received Organ Transplants

Usually we’d agree that it’s better to give than to receive, but we’re betting that these famous organ recipients were pretty thankful for these life-saving gifts.

33 Celebrities Who Have Killed People

It turns out that some very famous people have done some very bad things. We’re just reporting the facts, so please don’t shoot the messenger. Seriously. Please?

The Best Cures For Hangovers

Holiday parties = hangovers. There’s really no way around it. For the most effective remedies, you’ll want to consult this comprehensive list of foods you should eat the day after.

The Best Cities to Party in for New Years Eve

Partaaay! Some cities are famous for their NYE celebrations, but there are epic parties going on all over the world. We suggest you go forth and explore some of these destinations.

Toast The New Year With The Top New Year’s Eve Movie Scenes

According to these films, the best things to do on NYE include professing your love to a long time friend, hooking up with people you shouldn’t, and grand larceny. One of these things is not like the other…

That’s it for this year! From all of us here at Ranker, we wish you lots of holiday cheer!

by   Ranker
in Data Science, Opinion Graph, prediction, semantic search

Why Topsy/Twitter Data may never predict what matters to the rest of us

Recently Apple paid a reported $200 million for Topsy and some speculate that the reason for this purchase is to improve recommendations for products consumed using Apple devices, leveraging the data that Topsy has from Twitter.  This makes perfect sense to me, but the utility of Twitter data in predicting what people want is easy to overstate, largely because people often confuse bigger data with better data.  There are at least 2 reasons why there is a fairly hard ceiling on how much Twitter data will ever allow one to predict about what regular people want.

1.  Sampling – Twitter has a ton of data, with daily usage of around 10%.  Sample size isn’t the issue here as there is plenty of data, but rather the people who use Twitter are a very specific set of people.  Even if you correct for demographics, the psychographic of people who want to share their opinion publicly and regularly (far more people have heard of Twitter than actually use it) is way too unique to generalize to the average person, in the same way that surveys of landline users cannot be used to predict what psychographically distinct cellphone users think.

2. Domain Comprehensiveness – The opinions that people share on Twitter are biased by the medium, such that they do not represent the spectrum of things many people care about.  There are tons of opinions on entertainment, pop culture, and links that people want to promote, since they are easy to share quickly, but very little information on people’s important life goals or the qualities we admire most in a person or anything where people’s opinions are likely to be more nuanced.  Even where we have opinions in those domains, they are likely to be skewed by the 140 character limit.

Twitter (and by extension, companies that use their data like Topsy and DataSift) has a treasure trove of information, but people working on next generation recommendations and semantic search should realize that it is a small part of the overall puzzle given the above limitations.  The volume of information gives you a very precise measure of a very specific group of people’s opinions about very specific things, leaving out the vast majority of people’s opinions about the vast majority of things.  When you add in the bias introduced by analyzing 140 character natural language, there is a great deal of variance in recommendations that likely will have to be provided by other sources.

At Ranker, we have similar sampling issues, in that we collect much of our data at, but we are actively broadening our reach through our widget program, that now collects data on thousands of partner sites.  Our ranked list methodology certainly has bias too, which we attempt to mitigate that through combining voting and ranking data.  The key is not in the volume of data, but rather in the diversity of data, which helps mitigate the bias inherent in any particular sampling/data collection method.

Similarly, people using Twitter data would do well to consider issues of data diversity and not be blinded by large numbers of users and data points.  Certainly Twitter is bound to be a part of understanding consumer opinions, but the size of the dataset alone will not guarantee that it will be a central part.  Given these issues, either Twitter will start to diversify the ways that it collects consumer sentiment data or the best semantic search algorithms will eventually use Twitter data as but one narrowly targeted input of many.

– Ravi Iyer

by   Ranker
in interest graph, Market Research, Pop Culture

Hierarchical Clustering of a Ranker list of Beers

This is a guest post by Markus Pudenz.

Ranker is currently exploring ways to visualize the millions of votes collected on various topics each month.  I’ve recently begun using hierarchical cluster analysis to produce taxonomies (also known as dendograms), and applied these techniques to Ranker’s Best Beers from Around the World. A dendrogram allows one to visualize the relationships on voting patterns (scroll down to see what a dendrogram looks like). What hierarchical clustering does is break down the list into related groups based on voting patterns of the users, grouping like items with items that were voted similarly by the same users. The algorithm is agglomerative, meaning it is starts with individual items and combines them iteratively until one large cluster (all of the beers in the list)  remains.

Every beer in our dendrogram is related to another at some level, whether in the original cluster or further down the dendrogram. See the height axis on the left side? The lower the cluster is on the axis, the closer the relationship the beers will have. For example, the cluster containing Guinness and Guinness Original is the lowest in this dendrogram indicating these to beers have the closest relationship based on the voting patterns. Regarding our list, voters have the option to Vote Up or Vote Down any beer they want. Let’s start at the top of the dendrogram and work our way down.

Hierarchical Clustering of Beer Preferences

Looking at the first split of the clusters, one can observe the cluster on the right contains beers that would generally be considered well-known including Guinness, Sam Adams, Heineken and Corona. In fact, the cluster on the right includes seven of the top ten beers from the list. The fact that most of our popular beers are in this right cluster indicates that there is a strong order effect with voters more likely to select beers that are more popular when ranking their favorite beers. For example, if someone selects a beer that is in the top ten, then another beer they select is also more likely to be in the top ten. As we examine the right cluster further, the first split divides the cluster into two smaller clusters. In the left cluster, we can clearly see, unsurprisingly, that a drinker who likes Guinness is more likely to vote for another variety of Guinness. This left cluster is comprised almost entirely of Guinness varieties with the exception of Murphy’s Irish Stout. The right cluster lists a larger variety of beer makers including Sam Adams, Stella Artois and Pyramid. In addition, none of the beers in this right cluster are stouts as with the left cluster. The only brewer in this right cluster with multiple varieties is Sam Adams with Boston Lager and Octoberfest meaning drinkers in this cluster were not as brand loyal as in the left cluster. Drinkers in this cluster were more likely to select a beer variety from a different brewer. When reviewing this cluster from the first split in the dendrogram, there is clearly a defined split between those drinkers who prefer a heavier beer (stout) as opposed to those who prefer lighter beers like lagers, pilseners, pale ales or hefeweizen.

Conversely, for beers in the left cluster, drinkers are more likely to vote for other beers that are not as popular with only three of the top ten beers in this cluster. In addition, because of the larger size, the range of beer styles and brewers for this cluster is more varied as opposed to those in the right cluster. The left cluster splits into three smaller clusters before splitting further. One cluster that is clearly distinct is the second of these clusters. This cluster is comprised almost entirely of Belgian style beers with the only exception being Pliny the Elder, an IPA. La Fin du Monde is a Belgian style tripel from Quebec with the remaining brewers from Belgium. One split within this cluster is comprised entirely of beer varieties from Chimay indicating a strong relationship; voters who select Chimay are more likely to also select a different style from Chimay when ranking their favorites.  Our remaining clusters have a little more variety. Our first cluster, the smallest of the three, has a strong representation from California with varieties from Stone, Sierra Nevada and Anchor Steam taking four out of six nodes in the cluster. Stone IPA and Stone Arrogant Bastard Ale have the strongest relationship in this cluster. Our third cluster, the largest of the three, has even more variety than the first. We see a strong relationship especially with Hoegaarden and Leffe.

I was also curious as to whether the beers in the top ten were associated with larger or smaller breweries. As the following list shows,  there is an even split between the larger conglomerates like AB InBev, Diageo, Miller Coors and independent breweries like New Belgium and Sierra Nevada.

  1. Guinness (Diageo)
  2. Newcastle (Heineken)
  3. Sam Adams Boston Lager (Boston Beer Company)
  4. Stella Artois (AB InBev)
  5. Fat Tire (New Belgium Brewing Company)
  6. Sierra Nevada Pale Ale (Sierra Nevada Brewing Company)
  7. Blue Moon (Miller Coors)
  8. Stone IPA (Stone Brewing Company)
  9. Guinness Original (Diageo)
  10. Hoegaarden Witbier (AB InBev)

Markus Pudenz

by   Ranker
in Popular Lists

You Voted: Walmart Has the Worst Reputation

Walmart Food Drive

The news that a Walmart store in Ohio was holding a food drive to make sure that its own employees have enough food for the holidays struck a nerve with the Internet masses today. The story climbed to #1 on CNN Trends, spread quickly throughout the social media landscape and was mentioned by just about every mainstream media outlet–from Gawker, to Fox News to Business Insider.

Why the strong reaction? Many people though that Walmart is doing something “nice for humanity” by encouraging their employees to donate food to people who can’t afford it themselves. If only these people had jobs so they could afford it… oh that’s right, they do!

Despite being one of the richest companies in the world–they made $17 billion in profit last year–Walmart has been accused by many of failing to pay its workers a living wage, provide healthcare, or ensure that working conditions are safe. On the flip side, they do help billions of consumers worldwide stretch their paychecks by offering extremely low-priced goods.


These are probably some of the reasons why so many users upvoted Walmart to the #1 spot on Ranker’s list of Companies With the Worst Reputations. (If there are others, feel free to leave them in the comment section of the list!)

Other heavy hitters on the list include:

British Petroleum
 at #2, whose 2010 oil spill dumped 210 million gallons of oil into the Gulf of Mexico and pretty much ruined the local economy and ecosystem there.


Halliburton at #3, former Vice President Dick Cheney’s company that made billions of dollars from questionable deals related to the Iraq War.

Citigroup at #4, a financial institution so badly damaged by the financial crisis in 2008 that it had to be bailed out by the U.S. government 3 times . . . but was then able to pay its executives hundreds of millions of dollars in bonuses.


and Monsanto, at #5, a biotech corporation that has spent 20 years, millions of dollars and endless lawsuits against small farmers in an attempt to replace biodiversity with their own, patented GMO seeds. monsanto

 While the top 5 items on this list will continue to change as time goes by, corporations continue to screw things up, and you keep voting, it’s interesting to note how diverse the companies in these top 5 slots are. Ranker users are a mixed bunch–people from all over the world stop by to vote on lists about everything–from entertainment, science, politics, sports and much more. This diversity is reflected in the range of companies that users upvoted.  People who voted on this list care about issues ranging from workers rights, environmental, & social and economic inequality–a good representation of the most talked-about issues of our time!

If you haven’t already, head to the list of companies with the worst reputations and vote on which company you disagree with.