Attention on Weak Ties in Social and Communication Networks

TL;DR

This study empirically validates Granovetter's weak tie theory, showing that weak ties receive high attention possibly due to their informational value, with variations across different social media platforms.

Contribution

The paper provides the first quantitative analysis supporting both of Granovetter's hypotheses, linking attention to tie strength and importance in large-scale social networks.

Findings

Weak ties receive high attention, supporting their informational role.

Strong ties also attract high attention, indicating their importance.

Attention patterns vary across different social media platforms.

Abstract

Granovetter's weak tie theory of social networks is built around two central hypotheses. The first states that strong social ties carry the large majority of interaction events; the second maintains that weak social ties, although less active, are often relevant for the exchange of especially important information (e.g., about potential new jobs in Granovetter's work). While several empirical studies have provided support for the first hypothesis, the second has been the object of far less scrutiny. A possible reason is that it involves notions relative to the nature and importance of the information that are hard to quantify and measure, especially in large scale studies. Here, we search for empirical validation of both Granovetter's hypotheses. We find clear empirical support for the first. We also provide empirical evidence and a quantitative interpretation for the second. We show that attention, measured as the fraction of interactions devoted to a particular social connection, is high on weak ties --- possibly reflecting the postulated informational purposes of such ties --- but also on very strong ties. Data from online social media and mobile communication reveal network-dependent mixtures of these two effects on the basis of a platform's typical usage. Our results establish a clear relationships between attention, importance, and strength of social links, and could lead to improved algorithms to prioritize social media content.