Modelling Participation in Small Group Social Sequences with Markov Rewards Analysis
Modelling Participation in Small Group Social Sequences with Markov Rewards Analysis
复制标题
使用马尔可夫奖励分析对小组社交序列中的参与进行建模
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Gabriel Murray
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文献类型：
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作者：
Gabriel Murray
我们探索了一种新的计算方法来分析成员参与小群体的社会序列。使用一个复杂的状态表示相结合的对话行为类型，情感表达，和参与者的角色的信息，我们探索的序列状态与高水平的成员参与。使用马尔可夫奖励框架，我们将特定的状态与即时的积极和消极奖励相关联，并采用值迭代算法来计算所有状态的期望值。在我们的研究结果中，我们专注于属于团队领导者和项目经理的话语状态，这些话语状态非常有可能或不太可能导致团队其他成员的参与。
We explore a novel computational approach for analyzing member participation in small group social sequences. Using a complex state representation combining information about dialogue act types, sentiment expression, and participant roles, we explore which sequence states are associated with high levels of member participation. Using a Markov Rewards framework, we associate particular states with immediate positive and negative rewards, and employ a Value Iteration algorithm to calculate the expected value of all states. In our findings, we focus on discourse states belonging to team leaders and project managers which are either very likely or very unlikely to lead to participation from the rest of the group members.