Understanding why certain individuals work well (or poorly) together as a team is a key research focus in the psychological and behavioral sciences and a fundamental problem for team-based organizations. Nevertheless, we have a limited ability to predict the social and work-related dynamics that will emerge from a given combination of team members. In this work, we model vocal turn-taking behavior within conversations as a parametric stochastic process on a network composed of the team members. More precisely, we model the dynamic of exchanging the `speaker token' among team members as a random walk in a graph that is driven by both individual level features and the conversation history. We fit our model to conversational turn-taking data extracted from audio recordings of multinational student teams during undergraduate engineering design internships. Through this real-world data we validate the explanatory power of our model and we unveil statistically significant differences in speaking behaviors between team members of different nationalities.
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Multimodal Turn Analysis and Prediction for Multi-party Conversations
This paper presents a computational study to analyze and predict turns (i.e., turn-taking and turn-keeping) in multiparty conversations. Specifically, we use a high-fidelity hybrid data acquisition system to capture a large-scale set of multi-modal natural conversational behaviors of interlocutors in three-party conversations, including gazes, head movements, body movements, speech, etc. Based on the inter-pausal units (IPUs) extracted from the in-house acquired dataset, we propose a transformer-based computational model to predict the turns based on the interlocutor states (speaking/back-channeling/silence) and the gaze targets. Our model
can robustly achieve more than 80% accuracy, and the generalizability of our model was extensively validated through cross-group experiments. Also, we introduce a novel computational metric called “relative engagement level" (REL) of IPUs, and further validate its statistical significance between turn-keeping IPUs and turn-taking
IPUs, and between different conversational groups. Our experimental results also found that the patterns of the interlocutor states can be used as a more effective cue than their gaze behaviors for predicting turns in multiparty conversations.
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- Award ID(s):
- 2005430
- NSF-PAR ID:
- 10532343
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400700552
- Page Range / eLocation ID:
- 436 to 444
- Subject(s) / Keyword(s):
- Multi-party conversations conversational gesture understanding Multimodal interaction Machine learning Human-human interaction Empirical studies
- Format(s):
- Medium: X
- Location:
- Paris France
- Sponsoring Org:
- National Science Foundation
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Abstract This article introduces the concept of “topic territoriality,” a mechanism that governs participation in conversational spaces. When a discussion becomes prone to territorialization, individuals are more likely to claim topics (participating in discussions about topics they own as “stakeholders”) and defer (reducing participation in topics owned by others). They are also more likely to patrol topic boundaries (monitoring who is participating and confronting topic “intruders”). We document the operation of topic territoriality by analyzing 112,278 conversational turns on Weibo before and after a policy that reveals users’ broad geographic locations. We find that revealing these locations increased territorial behaviors, leading to more homogenous participation in conversations. Although the display of locations has improved the overall civility in language, the confrontations between stakeholders and intruders became more toxic. Our research emphasizes the impact of topic territoriality in online conversations and sheds light on the unintended consequences of social media policies.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3577190.3614139
