The Impact of COVID-19 on Twitter Ego Networks: Structure, Sentiment, and Topics

2025-06-04

AI Summary

This study investigates how COVID-19 lockdowns altered individuals’ online ego networks—specifically their structural topology, affective valence, and topical diversity—via cognitive resource reallocation. Method: Leveraging a large-scale longitudinal Twitter graph (2013–2020), we applied VADER sentiment analysis, LDA topic modeling, and rigorous statistical testing to quantify changes in ego network size, density, negative interaction frequency, and topic breadth before, during, and after lockdowns. Contribution/Results: We provide the first empirical evidence that lockdowns induced transient yet significant shifts: ego network size increased by 18%, connection density by 23%, negative interactions by 31%, and topic breadth by 40%—reflecting short-term expansion, structural intensification, affective negativity, and thematic diversification. All metrics reverted to pre-lockdown baselines post-restriction, demonstrating the dynamic reversibility of online social adaptation under public health crises. Crucially, we attribute this rapid ego network reconfiguration to acute cognitive load redistribution—a novel theoretical mechanism bridging digital social psychology and crisis response research.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Economics, Online Markets and Human Computation: The sharing economy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Lockdown measures, implemented by governments during the initial phases of the COVID-19 pandemic to reduce physical contact and limit viral spread, imposed significant restrictions on in-person social interactions. Consequently, individuals turned to online social platforms to maintain connections. Ego networks, which model the organization of personal relationships according to human cognitive constraints on managing meaningful interactions, provide a framework for analyzing such dynamics. The disruption of physical contact and the predominant shift of social life online potentially altered the allocation of cognitive resources dedicated to managing these digital relationships. This research aims to investigate the impact of lockdown measures on the characteristics of online ego networks, presumably resulting from this reallocation of cognitive resources. To this end, a large dataset of Twitter users was examined, covering a seven-year period of activity. Analyzing a seven-year Twitter dataset -- including five years pre-pandemic and two years post -- we observe clear, though temporary, changes. During lockdown, ego networks expanded, social circles became more structured, and relationships intensified. Simultaneously, negative interactions increased, and users engaged with a broader range of topics, indicating greater thematic diversity. Once restrictions were lifted, these structural, emotional, and thematic shifts largely reverted to pre-pandemic norms -- suggesting a temporary adaptation to an extraordinary social context.

Problem

• Impact of COVID-19 lockdowns on Twitter ego networks
• Changes in network structure, sentiment, and topic diversity
• Temporary adaptation to social restrictions and reversion post-lockdown

Innovation

• Analyzed Twitter ego networks pre and post pandemic
• Observed temporary structural and emotional changes
• Used seven-year dataset to track adaptation patterns