Valuable insights into an individual’s current thoughts and stance regarding behaviour change can be obtained by analysing the language they use, which can be conceptualized using Motivational Interviewing concepts. Training conversational agents (CAs) to detect and employ these concepts could help them provide more personalized and effective assistance. This study investigates the similarity of written language around behaviour change spanning diverse conversational and social contexts and change objectives. Drawing on previous research that applied MI concepts to texts about health behaviour change, we evaluate the performance of existing classifiers on six newly constructed datasets from diverse contexts. To gain insights in determining factors when identifying change language, we explore the impact of lexical features on classification. The results suggest that patterns of change language remain stable across contexts and domains, leading us to conclude that peer-to-peer online data may be sufficient to train CAs to understand user utterances related to behaviour change.