Social Isolation, Digital Connection: COVID-19's Impact on Twitter Ego Networks

TL;DR

This study addresses how COVID-19 lockdowns affected Twitter ego networks by analyzing seven years of user timelines to track online socialization under cognitive constraints. The authors construct ego networks using the contact-frequency metric $w_{uj}^{(i)}$ and identify intimacy rings with Mean Shift to form circles, enabling metrics such as active ego-network size and circle counts. They find a pronounced post-lockdown expansion in active ego networks and circles, especially in outer rings, while inner circles remain stable, with a reversion to pre-pandemic structure after restrictions lift. The work demonstrates a temporary reallocation of social cognitive capacity to online interaction during lockdown and provides quantitative insight into how disruptive events reshape online social graphs and their resilience.

Abstract

One of the most impactful measures to fight the COVID-19 pandemic in its early first years was the lockdown, implemented by governments to reduce physical contact among people and minimize opportunities for the virus to spread. As people were compelled to limit their physical interactions and stay at home, they turned to online social platforms to alleviate feelings of loneliness. Ego networks represent how people organize their relationships due to human cognitive constraints that impose limits on meaningful interactions among people. Physical contacts were disrupted during the lockdown, causing socialization to shift entirely online, leading to a shift in socialization into online platforms. Our research aimed to investigate the impact of lockdown measures on online ego network structures potentially caused by the increase of cognitive expenses in online social networks. In particular, we examined a large Twitter dataset of users, covering 7 years of their activities. We found that during the lockdown, there was an increase in network sizes and a richer structure in social circles, with relationships becoming more intimate. Moreover, we observe that, after the lockdown measures were relaxed, these features returned to their pre-lockdown values.

Figures (9)

• Figure 1: The ego network model.
• Figure 2: Distribution of active ego networks sizes
• Figure 3: Total number of tweets considered
• Figure 4: Mean values and 99% confidence interval of the ego network sizes in (a) and of the growth rate of their difference in (b).
• Figure 5: Distribution of ego networks' number of circles over the periods considered.