Paper by Luca Maria Aiello: “Social relationships are probably the most important things we have in our life. They help us to get new jobs, live longer, and be happier. At the scale of cities, networks of diverse social connections determine the economic prospects of a population. The strength of social ties is believed one of the key factors that regulate these outcomes. According to Granovetter’s classic theory about tie strength, information flows through social ties of two strengths: weak ties that are used infrequently but bridge distant groups that tend to posses diverse knowledge; and strong ties, that are used frequently, knit communities together, and provide dependable sources of support.
For decades, tie strength has been quantified using the frequency of interaction. Yet, frequency does not reflect Granovetter’s initial conception of strength, which in his view is a multidimensional concept, such as the “combination of the amount of time, the emotional intensity, intimacy, and services which characterize the tie.” Frequency of interaction is traditionally used as a proxy for more complex social processes mostly because it is relatively easy to measure (e.g., the number of calls in phone records). But what if we had a way to measure these social processes directly?
We used advanced techniques in Natural Language Processing (NLP) to quantify whether the text of a message conveys knowledge (whether the message provides information about a specific domain) or support (expressions of emotional or practical help), and applied it to a large conversation network from Reddit composed by 630K users resident in the United States, linked by 12.8M ties. Our hypothesis was that the resulting knowledge and support networks would fare better in predicting social outcomes than a traditional social network weighted by interaction frequency. In particular, borrowing a classic experimental setup, we tested whether the diversity of social connections of Reddit users resident in a specific US state would correlate with the economic opportunities in that state (estimated with GDP per capita)…(More)”.
