Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment

By Sinan Aral, Dylan Walker. Management Sciences, 2014

367 citations recorded by the source corpus.

We leverage the newly emerging business analytical capability to rapidly deploy and iterate large-scale, microlevel, in vivo randomized experiments to understand how social influence in networks impacts consumer demand. Understanding peer influence is critical to estimating product demand and diffusion, creating effective viral marketing, and designing “network interventions” to promote positive social change. But several statistical challenges make it difficult to econometrically identify peer influence in networks. Though some recent studies use experiments to identify influence, they have not investigated the social or structural conditions under which influence is strongest. By randomly manipulating messages sent by adopters of a Facebook application to their 1.3 million peers, we identify the moderating effect of tie strength and structural embeddedness on the strength of peer influence. We find that both embeddedness and tie strength increase influence. However, the amount of physical interaction between friends, measured by coappearance in photos, does not have an effect. This work presents some of the first large-scale in vivo experimental evidence investigating the social and structural moderators of peer influence in networks. The methods and results could enable more effective marketing strategies and social policy built around a new understanding of how social structure and peer influence spread behaviors in society. This paper was accepted by Alok Gupta, special issue on business analytics.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e7a184828af343e346589da44610c588a38ea402

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Sociology and Political Science

1. Amit Sharma
2. Ravi Bapna
3. Akhmed Umyarov
4. Eitan Muller
5. Dan Cosley
6. Renana Peres

In Statistical and Nonlinear Physics

1. Renana Peres
2. Amit Sharma
3. Ravi Bapna
4. Akhmed Umyarov
5. Eitan Muller
6. Dan Cosley

Citations in the corpus

• Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds (2016, background): “Past studies show that people perceive actions by different friends differently based on their relationship with them [4, 18, 31, 43].”
• Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks (2014, background): “For instance, researchers could introduce a particular functional form that explicitly models the shape of how the treatment effect changes with user’s degree (Aral and Walker 2014).”
• The effect of social networks structure on innovation performance: A review and directions for research (2019, background): “Embeddedness is defined as the extent to which network members share common peers (Aral & Walker, 2014), or common followers in a directional network (Peng, Agarwal, Hosanagar, & Iyengar, 2018).”