Next Article in Journal
A Review of Pneumatic Actuators Used for the Design of Medical Simulators and Medical Tools
Previous Article in Journal
Observing Collaboration in Small-Group Interaction
Open AccessArticle

Information Processing and Overload in Group Conversation: A Graph-Based Prediction Model

Department of Computer Information Systems, University of the Fraser Valley, Abbotsford, BC V2S 7M8, Canada
Multimodal Technologies Interact. 2019, 3(3), 46; https://doi.org/10.3390/mti3030046
Received: 14 May 2019 / Revised: 20 June 2019 / Accepted: 26 June 2019 / Published: 28 June 2019
(This article belongs to the Special Issue Recent Advances in Multimodal Conversational Systems)
Based on analyzing verbal and nonverbal features of small group conversations in a task-based scenario, this work focuses on automatic detection of group member perceptions about how well they are making use of available information, and whether they are experiencing information overload. Both the verbal and nonverbal features are derived from graph-based social network representations of the group interaction. For the task of predicting the information use ratings, a predictive model using random forests with verbal and nonverbal features significantly outperforms baselines in which the mean or median values of the training data are predicted, as well as significantly outperforming a linear regression baseline. For the task of predicting information overload ratings, the multimodal random forests model again outperforms all other models, including significant improvement over linear regression and gradient boosting models. However, on that task the best model is not significantly better than the mean and median baselines. For both tasks, we analyze performance using the full multimodal feature set versus using only linguistic features or only turn-taking features. While utilizing the full feature set yields the best performance in terms of mean squared error (MSE), there are no statistically significant differences, and using only linguistic features gives comparable performance. We provide a detailed analysis of the individual features that are most useful for each task. Beyond the immediate prediction tasks, our more general goal is to represent conversational interaction in such a way that yields a small number of features capturing the group interaction in an easily interpretable manner. The proposed approach is relevant to many other group prediction tasks as well, and is distinct from both classical natural language processing (NLP) as well as more current deep learning/artificial neural network approaches. View Full-Text
Keywords: group interaction; information overload; network models; natural language processing; social signal processing; multimodal interaction group interaction; information overload; network models; natural language processing; social signal processing; multimodal interaction
Show Figures

Figure 1

MDPI and ACS Style

Murray, G. Information Processing and Overload in Group Conversation: A Graph-Based Prediction Model. Multimodal Technologies Interact. 2019, 3, 46.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop