Understanding the Humans Behind Online Misinformation: An Observational Study Through the Lens of the COVID-19 Pandemic

TL;DR

This observational study investigates how humans behind online misinformation operate across domains during the COVID-19 pandemic. Leveraging a large-scale time-series design and robust causal inference, the authors trace misinformative behavior from pre-pandemic domains (politics, climate) to COVID-19, revealing that historical misinforming propensities strongly forecast current spread and that misinformation dynamics shift across domains under crisis conditions. The work combines multi-phase data collection, misinfo classifiers, and propensity-score matching to demonstrate cross-domain spillovers, persistent behavioral patterns, and psycholinguistic shifts, offering a human-centered, ecological framework for countermeasures, inoculation, and agile interventions. These findings support targeted, ethically mindful strategies that go beyond platform-level moderation toward addressing underlying drivers and domain-spanning information ecosystems.

Abstract

The proliferation of online misinformation has emerged as one of the biggest threats to society. Considerable efforts have focused on building misinformation detection models, still the perils of misinformation remain abound. Mitigating online misinformation and its ramifications requires a holistic approach that encompasses not only an understanding of its intricate landscape in relation to the complex issue and topic-rich information ecosystem online, but also the psychological drivers of individuals behind it. Adopting a time series analytic technique and robust causal inference-based design, we conduct a large-scale observational study analyzing over 32 million COVID-19 tweets and 16 million historical timeline tweets. We focus on understanding the behavior and psychology of users disseminating misinformation during COVID-19 and its relationship with the historical inclinations towards sharing misinformation on Non-COVID domains before the pandemic. Our analysis underscores the intricacies inherent to cross-domain misinformation, and highlights that users' historical inclination toward sharing misinformation is positively associated with their present behavior pertaining to misinformation sharing on emergent topics and beyond. This work may serve as a valuable foundation for designing user-centric inoculation strategies and ecologically-grounded agile interventions for effectively tackling online misinformation.

Figures (6)

• Figure 1: An overview figure summarizing the data collection methodology for our study. The pipeline has three main components pertaining to data collection and user classification. The number and text below each part explain the method.
• Figure 2: Sankey plots for the Misinformative (left) and Non-Misinformative users (right). Each plot presents the statistics related to users active (posted at least one tweet in the respective month) between specific "starting" and "ending" months.
• Figure 3: Kernel distribution plot of SMD values of co-variates in matching Control and Treatment users.
• Figure 4: Tendency to spread Non-COVID and COVID-19 misinformation among Control and Treatment users during Peri-COVID era.
• Figure 5: Persistence in behavior to spread COVID-19 misinformation among the Control and Treatment users during the Peri-COVID era. Bubble sizes are proportional to #users.