One of the more interesting topics surrounding the growth of mobile broadband traffic is the effect social networking has in driving traffic patterns and content concentration in mobile networks - and more specifically the impact on video traffic. There is considerable research underway in both industry and academic arenas to better understand how highly connected networks of individual users drive viewing behavior and popularity of content. A deeper understanding of these trends is important in order to effectively dimension the capacity needs of broadband networks but also the back-end data centers and storage networks that enable broadband content delivery. In addition, these insights are of high interest to content developers and advertisers. For mobile operators in particular, this understanding can assist in developing more adaptive networks that can better support these rapid and random "popularity flash" events, also called "social cascades" in social networking circles.
What is clear is that social networking sites are a dominant and growing source of video traffic. YouTube (ranked #1) and Facebook (ranked #4)1 are two key examples of popular social networking sites experiencing significant video uploading and video sharing. Recent analysis reveals the effect that highly connected, highly regarded users have on the popularity (as measured by number of views) of video content. Studies conducted on large data sets from popular sites have concluded that:
- Video sharing sites have a high overlap of common content. Duplicated videos or common subsets are produced/uploaded across multiple networking groups with the overall overlap being as high as 25% on a given day.
- Unlike traditional websites that rely on search engines, social links represent the primary method users find content on these social networking sites. Having someone you have a relationship with and trust recommend content makes it much more likely you will view that content.
- The number of social links a user has directly correlates with the popularity of content that is posted/recommended to the user's community. For example, in one study, users with 1,000 or more followers/friends routinely drove 10,000 - 1,000,000 views of a video. This effect accelerates geometrically as a progression of overlapping social network groups; first order followers/friends connect the content to other networks, and so on.
- More influential users have a disproportionate amplifying effect on popularity. If a content owner/advertiser can identify these highly connected and influential users they will see a direct effect in popularity of their content.
- The number of social links associated with a video far outweighs (greater than 10x) its user quality metrics or comments (like/dislikes, "thumbs up/down"...). Just being uploaded or referenced by a highly connected group will drive popularity independent of quality metrics.
- This popularity progression can vary widely. In some cases it can be characterized by long, sustained growth as organic sharing occurs slowly over time. In other cases, the growth and subsequent decay can be very fast as the content is shared rapidly across multiple networks and the social network site "most watched" list picks up the demand for the video, further accelerating its popularity.
All of these studies and analysis point to the importance of social networking and online communities in selecting what content is going to be popular on the Internet at any given time.
So what are the implications for mobile broadband operators?
The social cascade phenomenon of fewer content objects being viewed by more people - coupled with the fact that this content is increasingly composed of larger, bandwidth-intensive video objects - means operators must continue to add capacity to their networks or the multimedia experience we have come to expect from our 'smart' mobile devices will suffer. Solutions like IQstream Adaptive Content Optimization (ACO) provide operators with compelling ways to mitigate the effect of popular content flash events and content concentration, as well as help rebalance their capacity planning efforts.
Below are some related references:
- Characteristics and evolution of content popularity and user relations in social networks
- Impact of Social Network Structure on Content Propagation: A Study using YouTube Data
- Automatic Video Tagging using Content Redundancy
- Predicting the popularity of online content
1comScore Video Metrix: Top U.S. Online Video Properties by Video Content Views Ranked by Unique Video Viewers, February 2011