  Individuals experiencing unexpected distressing events, shocks, often rely on
their social network for support. While prior work has shown how social
networks respond to shocks, these studies usually treat all ties equally,
despite differences in the support provided by different social relationships.
Here, we conduct a computational analysis on Twitter that examines how
responses to online shocks differ by the relationship type of a user dyad. We
introduce a new dataset of over 13K instances of individuals' self-reporting
shock events on Twitter and construct networks of relationship-labeled dyadic
interactions around these events. By examining behaviors across 110K replies to
shocked users in a pseudo-causal analysis, we demonstrate relationship-specific
patterns in response levels and topic shifts. We also show that while
well-established social dimensions of closeness such as tie strength and
structural embeddedness contribute to shock responsiveness, the degree of
impact is highly dependent on relationship and shock types. Our findings
indicate that social relationships contain highly distinctive characteristics
in network interactions and that relationship-specific behaviors in online
shock responses are unique from those of offline settings.
