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Social scientists have conceptualised different dimensions of human relationships, including knowledge, identity, and social support. At times of crisis, such as during a pandemic, social relationships can be disrupted, affecting the nature of interactions individuals have with others within their community and beyond. Understanding how relationships change, in both structure and content, can reveal how communities adapt and which connections are most resilient. Existing research has examined network structure or communicative content in isolation, but rarely both in combination and over time across multiple communities. In this study, we systematically investigate the temporal evolution of social relationships using readily available social media data. In particular, we make two contributions. First, we develop a computational methodology that leverages ego-networks to study changes to interactions between individuals. Second, we apply this methodology to over a million X/Twitter messages posted by more than two hundred teachers, journalists, and academics in UK from the beginning of 2019 to the end of 2021. In so doing, we longitudinally study and compare changes in social relationships before, during, and after COVID-19 lockdown periods. While results vary across communities, and data sparsity limits generalizability, our findings suggest that out-community ties exhibited more stability than in-community ties over time.
Douglas et al. (Sun,) studied this question.