This study presents an approach for detecting connective language-defined as language that facilitates engagement, understanding, and conversation-from social media discussions.We developed and evaluated two types of classifiers: BERT and GPT-3.5 turbo.Our results demonstrate that the BERT classifier significantly outperforms GPT-3.5 turbo in detecting connective language.Furthermore, our analysis confirms that connective language is distinct from related concepts measuring discourse qualities, such as politeness and toxicity.We also explore the potential of BERT-based classifiers for platform-agnostic tools.This research advances our understanding of the linguistic dimensions of online communication and proposes practical tools for detecting connective language across diverse digital environments.