For the longest time, I’ve wondered what brings the extraordinary success of some user-generated content. Consider, for example, the top ten most popular YouTube videos of all time. The #1 video on the list is a simple one-minute clip of a little baby biting his British English-accented brother’s finger. But it has received a whopping 155+ million views, while your average YouTube video probably doesn’t get much more than a dozen passerby’s attention. Why such a huge difference?  I asked.  When I spoke with my friend Michelle Rogerson, she expressed the same curiosity.  So we decided to set out to answer our question.

To do this, we collected a random sample of slightly more than 100 videos from YouTube over the course of a week. These are all fresh new videos just uploaded onto YouTube, so that we can study their rise to popularity from scratch. We traced each video for a period of two months, recording the number of views and the average user ratings each day. We also collected a large number of characteristics for each video (see the figure below), including those related to the video content, to the video author, and to the network of users connected to the video author. We further recruited a group of individuals to rate each video on its production quality, educational value, and entertainment value, which are the three components of what we call “innate content quality”.

Equipped with all these data, we then used a technique called recurrent events analysis to see how these video characteristics affect the popularity of a video. Below are some of the main things we found:

• Authors with a large number of subscribers who each has only a handful of friends are in a better position than authors with a small number of subscribers who in turn may have a large number of friends.
• Once past an author’s direct network of subscribers, influence rather than simple reach becomes critical. We attribute this to the large amount of user-generated content (UGC) being passed around everyday and as a result our tendency to ignore most sharing unless they come from someone we are really close to or someone whose opinion we respect (opinion leadership is not dead!)
• What proportion of an author’s subscribers know each other also matters. A totally segregated set of subscribers does not help get the words out, but a group of subscribers where everyone knows everyone else is not optimal either. We found that the ideal connection ratio (termed connectivity or density in network analysis) was about 38%. Below this ratio, diffusion rate increases with connectivity up to the maximum, and then decreases after this threshold.
• Of the three innate quality components, entertainment value and educational value are equally important in determining a video’s popularity. Production quality, on the other hand, did not matter.
• But the biggest impact did not come from innate quality but from what we call “manifested quality”, which is quality information publicly available through other users’ ratings (i.e., the little stars underneath each YouTube video). Increasing the average rating by 1 star can lead to as much as 13.5% gain in diffusion rate.
• Age has a negative effect on diffusion rate, meaning that younger users’ contributions are more likely to be popular.
• An author’s past experience and success also carry over to the new content. More prolific authors and authors whose past contributions were more popular are more likely to see their new content popular.

Of course, with only one study, we are far from completely answering our initial question.  But what we found here suggest that there are indeed systematic differences among videos and authors that can help predict the success of future content. Carrying this over to other types of user-generated content such as tweets and consumer blogs, these findings and findings from future studies should help companies pour through the overwhelming amount of user-generated content available online and selectively invest effort in the ones that are most likely to become popular.

What do you think?  I’d love to hear your thoughts.  Is there anything important that we are missing? If you are interested in more details about our study, you can download our working paper at http://www.yupingliu.com/files/papers/liu_rogerson_ugc_diffusion.pdf.

If you are involved in social media or viral marketing, most likely you have wondered how to increase the passing-along of your viral content. My co-author Michelle Rogerson and I have been wondering about the same question too in our research project on the spreading of user-generated content online. As the starting point, we conducted an exploratory survey to find out people’s general tendency to share information online and what makes them more or less likely to share information with others. Using snowballing technique, we were able to gather responses from 156 Internet users.  These users’ ages ranged from 18 to 62 with a median age of 30.  46% of these users were males and 54% were females. Here I share with you some key findings from the survey.

“If my friend shares something with me, I will view it. But don’t really expect me to pass it on.”

We asked our respondents how likely they are to view information shared by someone they know, and over 60% of them agreed that it is quite likely (7 or higher on a 10-point scale).  This is good news because in the case of viral campaigns, encouraging people to share information with their friends is likely to increase the reach of the campaign. The bad news we found, however, is that way fewer of them would further pass on the information to their respective friends.  Less than 20% of them said they are likely to pass on information shared with them by their friends.  Interestingly, when asked the same question about information consumers found online themselves rather than shared by their friends, those who selected likely to pass on information increased to about 30%.  The lesson here is that first-order word-of-mouth (consumers passing on information they found themselves) is more likely to happen than second-order word-of-mouth (consumers passing on information that are found by their friends). Therefore, companies engaging in word-of-mouth campaigns should still try to spread the word to as many “seeds” as possible rather than counting on a few starting points.

“Make me believe that the information is relevant to my friends and I will pass it on.”

The survey contained an open-ended question asking the respondents to list the factors that would make them more likely to share information online.  The dominant reason listed (by 35% of the sample) was relevance to the friends that they are passing the information on to.  This is perhaps not surprising considering that few of us want to jam our friends’ inbox with junk information.  For companies, this means an opportunity to encourage passing-along by demonstrating the content’s relevance to a consumer’s social circle.  Financial incentives offered to friends by some referral programs is an example of this approach. The second most widely listed reason was something funny.  Apparently, we as human beings like to share laughter with others.  Below are the top five reasons the respondents cited ranked by frequency:

1. Relevance to those sharing information with
2. Humor
3. Relevance to oneself
4. Importance/worthiness of information
5. Unusual/unique information


Opinion leaders share more information but are also more likely to seek advice.Studying information sharing is not complete without considering opinion leaders, those individuals that are on the cutting edge and are likely to influence other people’s opinions.  We found that being an opinion leader increases the likelihood to share information with others by 38%, perhaps partially explaining why these people are opinion leaders in the first place.  While this finding seems rather obvious, what is not so obvious is the finding that opinion leaders are also more likely to seek advice from others such as family, friends, and neighbors. Compared with regular individuals, opinion leaders are 25% more likely to seek advice from others. This finding is important because we have often seen the argument that the right way to treat social media is to be social (in other words, interacting with others).  Our study finds concrete support for that.  A true opinion leader does not just broadcast information to others but also listens closely and actively seeks out others’ feedback.

As we move forward to the next stage of the research project, we would love to hear your thoughts.  What makes you more likely to share stuff with other people?  As a company, how do you manage your viral campaign content and seeding process so that it can create the maximum ripple effect?