The researchers delved into the linguistic content that distinguishes messages with varying levels of verbal person centeredness (PC). Using transcripts from support providers trained to enact different levels of PC, they analyzed them with the Linguistic Inquiry and Word Count (LIWC) to determine whether computerized analysis can complement human coders when coding supportive conversations.
The results demonstrate that several categories in the LIWC dictionary differ systematically as a function of conversational PC level, with pronouns, social process, cognitive process, anxiety, and anger words being the most reliable predictors of which level of the PC hierarchy an interaction represents.
This study's implications shed light on the lexicon of conversations that vary in PC, demonstrating the potential for computerized analysis to complement human coders in coding supportive conversations. By better understanding the linguistic content of messages with distinct levels of PC, this research can improve our ability to communicate effectively in supportive interactions.