Reverse Network Effects and Social Network Platforms
In our course recently, we have been studying how an individual’s actions can affect the outcome of a larger group of people. These effects are called network effects. Network effects can greatly affect the actions of an individual versus when an individual makes their own decision. In an article featured on Wired.com, Sangeet Paul Choudary discusses how network effects and social media interact. Many social media platforms, including but not limited to Facebook, YouTube, and Twitter, benefit from network effects, as the more users they have, the more value each individual user gets. However, Choudary explores the idea of social media networks becoming too large to become useful to their users; i.e. a drop off in network effects as user count becomes too large.
First, Choudary explains there being a strong correlation between user count and social media value. A positive feedback loop is created in the “rich-get-richer” process, in which as user count increases, then social media value increases, so user count increases, and a loop is formed. To specify the term “value” for a social network, Choudary discusses the ideas of connection, content, and clout. Connection value is the ability of a user on a social network to connect with other users, who can spread content (examples would be YouTube videos or photos on Flickr). As user count increases, more and more content is spread. The final value is clout value, which is for important or influential users to gain more influence by using the social media platform to spread their content.
In terms of what Choudary calls the “reverse network effect”, where network effects fail as user count increases on social media, he discusses how each of the above values are lowered as social media user count increases. For connection value, as user count increases, the quality of the connections between users may decrease; while it may be true that the user has more connections, they may have less strong connections and more weak connections. For content value, the content quality may decrease as user count goes up, or it could be that curated and personalized content may start to become unfeasible. For social networks that build their base in personalized content, it may be wise to limit users. If this does not occur, relevant content may become cluttered with irrelevant content. In terms of clout value, Choudary explains that social networks such as Twitter are biased against later users of the platform; as later users join, they are recommended people to follow and connect with, but may find it harder to develop their own following.
Choudary concludes by espousing that social networks need to maintain a high “signal-to-noise” ratio to remain relevant. That is, as number of users in a network increase, the ratio of valuable content to irrelevant content needs to remain high, or else the value of the social network could decrease.