What You Say in the Conversation Affects the Flow: Building a Model for Conversational Flow Using NLP Methods

"No Man Is an Island" - John Donne (1624)
Humans are inherently social beings; we interact with each other constantly. Conversations, one of the most prevalent social behaviors, play crucial roles in our lives. They allow us to connect, communicate, build relationships, and achieve our goals. In organizations, conversations are fundamental for effective collaboration and decision-making. Successful conversations build trust, foster positive relationships among team members, improve team dynamics, and lead to better problem-solving and innovation, ultimately driving better performance towards collective goals. However, not all conversations are easy to navigate. Various challenges arise when individuals interact and work together, potentially impacting workplace relationships and performance negatively. For instance, initiating conversations with strangers at networking events can be daunting. In teams, conflicts may occur due to differences in personality, communication styles, or goals, and individuals may make mistakes that affect their team's performance or relationships. Previous research on conversations has been limited due to the difficulty of capturing and analyzing natural conversations.
In my research, I aim to combine cutting-edge methods such as natural language processing (NLP), machine learning, and experiments to uncover the underlying mechanisms of human conversations and develop practical interventions to help individuals navigate important conversations in everyday life and organizational settings.
My first paper "What You Say in the Conversation Affects the Flow: Modelling Conversational Flow Using NLP Methods" has been accepted for academic presentations at the Royal Society scientific meeting, the International Association for Relationship Research annual conference, and the Towards the Future of AI conference in 2022. In this study, I build on recent research on conversational flow and use advanced computational methods to investigate how the content of a conversation influences its flow. I analyze real-world networking conversation data and develop practical interventions to improve conversational flow in networking.
In my Second Paper "Understanding Self-Focus and Other-Focus in Conversations", I investigate the fundamental dimension of communication: self-focus versus other-focus. In conversations, we sometimes focus more on ourselves (e.g., redirecting topics to ourselves, bragging) or on our conversation partner (e.g., asking questions, making effective responses). These focuses can be reflected in our words and lead to substantial intrapersonal and interpersonal consequences. Previous research has linked conversational narcissism to lower social attraction (Vangelisti et al., 1990) and found that first-person singular pronoun use relates to speakers' negative emotionality (Berry-Blunt et al., 2021). Building on these works and leveraging advanced computational methods, I aim to identify diverse linguistic features and create a comprehensive understanding of self-focus and other-focus tendencies in conversations.
As part of this project, I will visit Harvard Business School to collaborate with my collaborators and experts in this research field, collect data from real conversations in the behavioral lab, and further develop the research. This collaboration is crucial for data collection, analysis, and paper development. Therefore, I will apply for OSFW, as the visit is essential for the advancement and completion of my research projects.
In summary, my research leverages cutting edge methods to understand human conversations by unpacking decision-making processes in difficult social situations and develop interventions to improve interactions and relationships in everyday life and organizational contexts.