Social Ratings: Scaled Ratings Vs. Booleans

For years now, it has been a commonality for social media sites to rate content and commentary on a sliding scale. Sites like YouTube and Yelp allow users to rate and review content on a five-point scale, while sites like StumbleUpon and BuzzFeed want to just know if you like it or not.

Scaled ratings

Historically, stars have represented a scale of ratings on popular sites such as YouTube. People give five stars to videos they love and a single star to videos they dislike. YouTube has now revealed how rare it is to receive feedback from anything in the middle. In a recent blog post, they shared recent studies that display the skewed findings. Below is a graph of which star ratings are the most popular:

As you can see, it is very rare for anyone to provide feedback in the middle. Apple’s App Store also faces a similar conundrum.

The five-point scale seems to be too restrictive and allows for little nuance while a percentage scaled, based out of 100, seems to be too large. Usually, scores almost never fall below 50%, because most people innately think of education scoring and its 90 = A, 80 = B scheme when using 100 percentage scales.

The the 10 point scale seems most ideal. It provides enough room to make important distinctions, but not so much room it throws off the accuracy of the scale. However, let’s explore boolean ratings a bit.

Boolean Ratings

A boolean rating is defined by a simple “yes/no” standard. Most social voting sites follow this standard. If you don’t like the content, you vote it down or bury it while thumbing up or voting content you do like. StumbleUpon and Digg.com have taken this a step further by trying to understand why you are voting down the content. They allow you to select if you are burying or thumbing down the content because it is spam, content you dislike, duplicate content, etc. This additional information gives their algorithms a better feel for why it wasn’t well received.

Judging by the behavior of most users on the five-point rating system, the natural conclusion would be to rid any scaled rating systems with boolean voting. This creates a problem for people not wanting to give a full thumbs up or vote for some minuscule reason. So maybe adding a way to vote in the middle, with a sideways thumb, half-vote, or something similar should solve all the world’s problems at that point, right? Feedback What do you, as social media users, feel is a good metric for rating content?

Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.

Related Topics: Channel: Social | Search & Social | Social Media Marketing

About The Author: is the Online Marketing Strategist for People for the Ethical Treatment of Animals (PETA) as well as a speaker, author of A to Z: Social Media Marketing, and entrepreneur. You can follow him on @JordanKasteler.

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• Avintrue

I like the idea of Boolean Ratings. It obviously follows the users trends. A sideways thumb is almost a bit redundant because it basically says that the person reading the info didnt feel either way about it. I guess this would help to determine between people not interested in giving feedback and those who felt stuck in between on the info. But for the most part you could just substitute this for people who read the post but didnt feel either way strong enough to add a bad or good rating. I hope that makes sense.

• http://outingmyinnergeek.wordpress.com wjpeters

I’d like to see some harder stats incorporated into ratings… like number of visits or average time spent on a post to capture those middle ground folks who may very well read a page or article in its entirety, but not go any further to like/dislike it, rate it, tweet it, share it, etc.

• http://www.webwordofmouth.net psherland

Looks to me like you’re trying to treat qualitative data like quantitative data, which is a major sin in statistics. For example, you can’t say that someone who leaves a four-star rating likes a video twice as much as someone else who leaves a two-star rating. The ratings are ordinal — meaning that someone who rated a video as five stars likes the video more than a video he or she rated as four stars. However, you don’t know whether there was a significant difference for the reviewer between the five and four star videos or a negligible difference. Whether you use a five-star or a ten-star scale, you have to resist the temptation to calculate \average ratings\ with this kind of data. Even the graph should not depict the data as continuous — it should be displayed as a bar graph.

• EkOz

IMO the pitfall in how Youtube handles ratings is that they only desplay an average rating. With no details whatsoever. So for the surfer the question isn’t – How much should I rate it? – but – Do I like this rating, or do I want to increase it or decrease it?
An alternative would be to display some bar chart but it may just look complicated and confuse the users.

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