Social Signal Processing and Computational Modelling for Small Groups

该项目的目标是开发智能系统和计算模型，用于分析和支持小组互动和团队协作的多种模式 ** 社会信号识别模型 * 该项目这一部分的任务是从小组讨论中获取话语或句子，从中提取言语和非言语特征，并使用机器学习模型自动识别社交信号。这些社会信号是与群体决策有关的可观察行为，例如积极和消极情绪的表达、建议、协议、决定、问题和发言权。我们用于机器学习的特征将主要涉及语音行为，包括话语的语言内容，以及韵律、笑声和停顿等非语言特征。我们将研究这些语言特征与多层神经网络相结合的有用性。由于这种深度学习模型通常需要大量的训练数据才能最有效，并且可用的组交互数据量有限，因此我们将从相关领域（如电子邮件，社交媒体和即时消息）中查看域适应。我们将开发用于解释深度学习预测的信息可视化技术。群体预测模型/复杂适应系统 * 这个项目的组成部分涉及对群体现象进行时间预测，例如整体群体表现，群体生产力，成员满意度，群体凝聚力，新兴领导力和整体优势水平。我们将使用社交网络分析（SNA）算法来建模决策任务期间社会结构如何演变。我们将使用基于代理的建模（ABM）来模拟具有特定会话策略的个体如何导致复杂的群体行为。SNA和ABM都是将小群体视为复杂适应系统的特别有用的计算工具。在这样的系统中，个体代理相互适应并相互影响，导致复杂的群体行为，包括自组织，紧急领导，突然的相变和对初始条件的敏感性。** 回溯模型/隐藏的相互作用 ** 在这个项目中，我们将研究在直接观察小组有限的情况下分析小组互动和动态的方法。在这些情况下，我们可能希望使用有限的数据来预测过去一定发生了什么，以便将群体带到那个状态，这需要追溯模型。例如，我们可能有来自会议的工件，如参与者笔记，电子邮件和演示幻灯片，并希望预测不同成员在会议中的活跃程度，或者他们表达的情绪是什么。** 该项目还将包括收集和注释群体互动数据。**

The goal of this project is to develop intelligent systems and computational models for the analysis and support of small group interaction and team collaboration in multiple modalities******Social Signal Recognition Models******The task in this part of the project is to take utterances or sentences from a group discussion, extract verbal and nonverbal features from them, and automatically recognize social signals using machine learning models. These social signals are observable behaviours that relate to group decision-making, such as expression of positive and negative sentiment, proposals, agreement, decisions, questions, and floor-holding. The features we utilize for machine learning will primarily relate to vocal behaviour, including the linguistic content of utterances, and nonverbal features such as prosody, laughter, and pauses. We will investigate the usefulness of these language features in conjunction with multi-layer neural networks. Since such deep learning models typically require a large amount of training data to be most effective, and the amount of available group interaction data is limited, we will look at domain adaptation from related domains such as email, social media, and instant messaging. We will develop information visualization techniques for explaining deep learning predictions.******Group Prediction Models / Complex Adaptive Systems******This project component involves making temporal predictions about group phenomena, such as overall group performance, group productivity, member satisfaction, group cohesion, emergent leadership, and overall dominance levels. We will use social network analysis (SNA) algorithms for modelling how social structure evolves during the decision-making task. We will use agent-based modelling (ABM) to simulate how individuals with particular conversational strategies can lead to complex group behaviour. Both SNA and ABM are particularly useful computational tools for treating small groups as complex adaptive systems. In such systems, individual agents adapt to each other and influence each other, leading to complex group behaviour that can include self-organization, emergent leadership, abrupt phase transitions, and sensitivity to initial conditions. ******Retrodiction Models / Hidden Interactions******In this project component, we will investigate methods for analyzing group interaction and dynamics in situations where direct observation of the group is limited. In those cases, we may want to use the limited data to predict what must have occurred in the past in order to bring the group to that state, requiring retrodictive models. For example, we may have artifacts from a meeting, such as participant notes, emails, and presentation slides, and want to predict how active different members had been in the meeting, or what their expressed sentiment was. ******The project will also include collection and annotation of group interaction data.**