Causal Language in Post Titles Shapes Deeper Topological Structures of Online Conversations

Causal reasoning is fundamental to human understanding and information organization. People prefer causal explanations because they offer coherence, predictability, and a sense of control. Conversational structures shape how knowledge and perspectives are shared, validated, and amplified in networked publics. Understanding the structural effects of causal language can reveal pathways to fostering deeper, more meaningful interactions online. In this work, we investigate how causal language influences the topology and temporal evolution of discussion threads in online conversations with a dataset of 17 million posts across 200 subreddits in 2023 on Reddit. Our results show that causal language is consistently associated with deeper, more sustained conversations, with effects emerging early in the lifecycle of a thread, as demonstrated through a counterfactual experiment. Importantly, emotional responses do not differ substantially between causal language and non-causal language, suggesting that structural depth arises from framing itself rather than affective escalation. A lightweight qualitative analysis shows that causal framing titles prompt users to elaborate more with reasoning and contribute personal experiences, supporting deeper multi-turn exchanges. These findings suggest that causal language acts not merely as a stylistic device, but as a cognitively grounded and structurally influential signal that shapes the topological structures of online conversations.

CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing.