Strong & Weak Ties in Social Media
The Strength of Weak Ties is a widely cited article written by Mark Granovetter, a current professor at Stanford. Strong ties can be defined as a “combination of the amount of time, the emotional intensity, the intimacy … , and the reciprocal services,” (Granovetter, 1361). Granovetter argues that if two friends, let’s call them A and B, have a strong bond, and A has a strong bond with C, there is a high chance that B and C also have a strong bond (Granovetter, 1362).
These social ties result in interesting patterns of diffusion of information. Because those with strong ties tend to know the friends of their friends, groups can become self contained, without bridges or links to other groups of people. That means that strong ties can stop the diffusion of information, whereas weak ties will spread the information to new groups of people.
Granovetter analyzes The Urban Villagers, by Herbert Gans, and talks about how weak ties can help when organizing communities. Granovetter argues that the West End community was too fragmented to organize. This fragmentation can’t be seen on the micro level, but is obvious on the macro level because there are multiple groups of friends, without any bridges or links between them. However, on the micro level, it’s very difficult to see the lack of bridges and links.
Herbert Gans and Granovetter then both wrote a response to each other. Gans argued that Granovetter put too much of an emphasis on the weak ties. Although weak ties may have played a part in the lack of community organization, Gans claims that historical, cultural, and political reasons played a much larger part (Gans, 526). Granovetter then responded, saying that networks of strong and weak ties can affect the historical, cultural, and political variables ([2] Granovetter, 530.
Online Ties
Many social interactions happen online, specifically on social media platforms. In 2008, Facebook users had an average of 300 friends (Lampe et al., 724). However, most people can only maintain around 150 relationships, with 50 of those being close relationships, and 5 of those being intimate relationships (Konnikova). With 300 friends on Facebook, around half of these connections are weak ties, and people that the user doesn’t actively engage with.
Facebook’s Current Use of Ties
Gilbert and Karahalios investigated if a users activity on facebook could predict strong and weak ties. They took seven different measurements of a strong tie, including four mentioned by Granovetter, and assigned each different aspects of facebook use(Gilbert et al. pg 213). They found that they could predict (within one point of accuracy on a 10 point scale), how close a participant would rank a friend versus how facebook measurements would rank the closeness of a participant and friend (Gilbert et al., 216).
Currently, facebook does not differentiate between these different ties in terms of what content can be viewed. Since we now know that facebook usage can predict strong and weak ties, we could use this prediction to determine what content the user views, based on who they are close to. I believe that the facebook newsfeed algorithm does something similar to this already, based on who you interact with more, and what content you like, comment, and share. Facebook could also focus on privacy, by allowing a user to configure their privacy settings based on strong and weak ties. A user may want only close friends to see a post about a hardship they have gone through, but are okay with everyone seeing photos from their recent vacation. Instead of going through their entire friends list, they could easily choose friends (strong ties) or acquaintances (weak ties).
Social Algorithms
The idea of social ties fit into how social algorithms work. As mentioned above, the Facebook algorithm often chooses what content to show you based off of what you are going to interact with. If a user tends to interact with one individual more often, it can be assumed that they will be shown more posts by that individual. In a class, I looked into the facebook newsfeed algorithm, and how users actively manipulate the algorithm so that they can see the content that they are interested in. For example, a user might interact with a family members content, even though they may not normally, because it isn’t showing up on their newsfeed and they want to see it. This is where basing content shown on social ties, may work better than basing content on what users tend to interact with.
Granovetter, M. S. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360–1380.
Gans, H. J. (1974). Gans on Granovetter’s” Strength of Weak Ties”. American Journal of Sociology, 80(2), 524–527
[2] Granovetter, M. (1974). Granovetter replies to Gans.
Lampe, C., Ellison, N. B., & Steinfield, C. (2008, November). Changes in use and perception of Facebook. In Proceedings of the 2008 ACM conference on Computer supported cooperative work (pp. 721–730). ACM.
Konnikova, M. (2014, October 7). The Limits of Friendship. Retrieved April 28, 2018, from
https://www.newyorker.com/science/maria-konnikova/social-media-affect-math-dunbar-number-friendships
Gilbert, E., & Karahalios, K. (2009, April). Predicting tie strength with social media. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 211–220). ACM.
