Aggregating narratives on oil and gas from opposing advocacy groups: Revealing temporal shifts in narratives through text mining and network analysis

Abstract Existing Narrative Policy Framework (NPF) research often views policy narratives in isolation. Applying structural topic modeling (STM) and social network analysis (SNA) to social media data, this paper aggregates narratives to reveal the dynamics of narratives used by opposing advocacy groups. It examines how four advocacy groups with different policy stances support or oppose oil and gas development through X (formerly known as Twitter) from 2009 to 2023. STM reveals six prominent narratives associated with different groups. While the pro‐oil and gas groups highlight policy benefits including job creation, energy security, energy independence, and energy sufficiency, the anti‐oil and gas groups focus on policy costs such as air pollution and health threats. Moreover, the narratives have evolved over time. Notably, the pro‐oil and gas advocacy groups have increasingly emphasized energy leadership over job creation overall and have had more outgoing connections during the Obama and Biden presidencies, while the anti‐oil and gas advocacy groups have prominently highlighted wildlife threats and have had more interactions with other actors during the Trump presidency. By incorporating STM and SNA methodologies into the NPF, this analysis expands research on narratives in energy studies and challenges us to consider narratives collectively rather than in isolation.