Personality Differences Drive Conversational Dynamics: A High-Dimensional NLP Approach (2024)

Abstract: This paper investigates how the topical flow of dyadic conversations emerges over time and how differences in interlocutors’ personality traits contribute to this topical flow. Leveraging text embeddings, we map the trajectories of conversations between strangers into a high-dimensional space. Using nonlinear projections and clustering, we then identify when each interlocutor enters and exits various topics. Differences in conversational flow are quantified via , a summary measure of the “spread” of topics covered during a conversation, and , a time-varying measure of the cosine similarity between interlocutors’ embeddings. Our findings suggest that interlocutors with a larger difference in the personality dimension of openness influence each other to spend more time discussing a wider range of topics and that interlocutors with a larger difference in extraversion experience a larger decrease in linguistic alignment throughout their conversation. We also examine how participants’ affect (emotion) changes from before to after a conversation, finding that a larger difference in extraversion predicts a larger difference in affect change and that a greater topic entropy predicts a larger affect increase. This work demonstrates how communication research can be advanced through the use of high-dimensional NLP methods and identifies personality difference as an important driver of social influence.

## introduction each conversation we take part in is an opportunity for others to influence us and for us to influence others. among other categorizations, this social influence can be cognitive, such as introducing someone to a new topic to think about, or affective (related to emotion), such as making someone feel more positive by the end of a conversation as compared to before. however, not every conversation will induce changes in interlocutors' cognition or affect. how can we determine which ones will? we might consider whom one talks to as an important predictor of the conversation's resulting social influence. aspects of an interlocutor's personality are reflected in the language they use @xcite . thus, conversation can serve as a mechanism through which differences in personality drive social influence. large conversational corpora and the computational tools developed for working with linguistic data open new opportunity to test theories of social influence at a large scale. identifying the mechanisms and drivers of social influence in real-world data is necessary for furthering our basic understanding of how conversations serve as vehicles for cognitive and affective change. this foundational knowledge can help inform downstream theories of applied social influence tasks, such as negotiation @xcite and persuasion @xcite . in this paper, we investigate how differences in interlocutors' personality traits relate to the content and outcomes of a conversation. we focus on how personality differences relate to the topical flow of a conversation, the linguistic alignment between its interlocutors, and the interlocutors' subjective ratings of their affect. through this analysis, we set forth a method of characterizing conversational behavior through high-dimensional text embeddings, projection into a low-dimensional space, and topic clustering. ## data analysis through application of text embeddings, projection, clustering, and calculations described above, we obtained a set of summary measures that describe aspects of conversational flow (topic entropy; level, linear change, and quadratic change in linguistic alignment), the personalities the interlocutors brought with them into the dyadic conversation (mean and difference in five personality traits), and the affective states of the dyads (pre-post mean and change in affect valence) across 1655 conversations. descriptive statistics of the summary dataset used for subsequent statistical modeling are shown in using these summary data, we investigated the relations among conversation metrics, personality, and affect using linear regression models. data analysis and statistical modeling were done using version 4.3.3 of the r programming language (r core team, 2021). key findings are reported in the next section. ## discussion the candor corpus @xcite opens unique opportunity to examine unscripted communicative behavior between strangers. the diversity in the participants' personality traits and linguistic behavior allowed us to investigate some of the potential pathways through which people influence each other via dyadic conversations. the large sample size (over 1450 participants taking part in over 1650 conversations) makes the findings reasonably generalizable (e.g., to the u.s. population). further, the relatively long conversation length (25+ minutes) supports a robust analysis of how discussion topics and linguistic behavior change over the course of a conversation. our analysis supports several key findings about how between-interlocutor differences in personality influence conversational dynamics, as well as how those conversational dynamics relate to interlocutors' affect. findings from model 1 indicate that interlocutors who differ in their openness will spend more time talking about more topics. this result may be a consequence of the more open interlocutor influencing the less open interlocutor to explore new topics. findings from model 2 indicate that interlocutors who differ in their extraversion will experience a more pronounced divergence in language use over the course of a conversation. this result may reflect a lack of effective social influence between such pairs of interlocutors. findings from model 3 indicate that interlocutors who differ in their extraversion also differ in how much their affect changes from before to after their conversation. this result supports the theory that such interlocutors do not subjectively experience the conversation in the same way. finally, findings from model 4 indicate that when interlocutors spend more time discussing more topics, they experience a greater boost in affect from the conversation. this result highlights the positive affective social influence of conversations with high topic entropy. taken together, the findings support the hypothesis that the dynamics of a conversation, measured by topic entropy and linguistic alignment, mediate the influence of interlocutors' personality differences on their affective responses to the conversation. in other words, personality differences drive social influence, especially affective social influence, through the mechanism of conversation. we identify openness and extraversion as big five personality dimensions that are particularly consequential for social influence. llms often exhibit high openness and low extraversion, thus it will be important to consider personality's role in llm-based social influence as human-machine conversations become more commonplace. ## conclusion in this paper, we examined if and how differences in interlocutors' personality traits were related to differences in the dynamics of naturalistic conversations. our findings suggest that personality differences are associated with a conversation's topic entropy and how interlocutors' linguistic alignment changes over time. we also illustrated a process by which personality differences influence conversational dynamics, which in turn influence interlocutors' affective states. at a more general level, this work demonstrates new possibilities to engage in quantitative conversation analysis by leveraging text embeddings, projection, and clustering to track interlocutors' movements throughout semantic space over the course of a conversation.