A Temporal Dynamics Framework and Methodology for Computationally Intensive Social Media Research

Abstract

The growing availability of expansive social media trace data (SMTD) offers researchers promising opportunities to create rich depictions of societal and social phenomena. Despite this potential, research analysing such datasets often struggles to construct novel theoretical insight. This paper argues that holistically incorporating temporality enhances data collection and data analysis, thereby facilitating process theory construction from SMTD. Recommendations to integrate temporality are outlined in the proposed Temporal Dynamics Framework and Methodology (TDFM). We apply the TDFM to investigate the temporal dynamics of mental health discourse on Twitter (now X) across different phases of the COVID-19 pandemic, theoretically framed in the context of innate psychological needs satisfaction. The findings reveal dynamic shifts in social media use, indicating that different phases of the pandemic triggered dynamic shifts in the needs motivating, and being motivated by, social media use. This illustrative case reflectively evaluates the usefulness of the TDFM in contextualising SMTD collection, analytical strategies, and process theory construction by incorporating a dynamic perspective on time.