BIGDATA: IA: Harnessing Language and Interaction Dynamics at Multiple Scales to Maximize the Benefits of Group Interaction

因特网使人们能够在从小团体到全球社交网络的各种规模上进行新的互动。这些新形式的互动所产生的数据一直很难整合，因为它们如何跨越如此广泛的尺度;然而，如果没有这种整合，我们就无法理解微观层面的群体互动如何形成宏观尺度的行为，或者宏观层面的因素如何塑造小群体互动。数据科学的一个关键挑战是弥合微观和宏观层面之间的差距;这一挑战的一个关键但相对未被探索的部分在于缺乏概念上有用的模型和技术。该项目调查不同规模的在线社交互动数据，并通过考虑到全球规模以及中间的中尺度互动（构成数百或数千参与者之间的互动），开发弥合极端情况的方法。开发处理中观层面数据的方法不仅本身就提出了科学上丰富的问题，而且还允许在微观和宏观层面的现象中建立变革性的理论。该项目汇集了来自广泛背景的研究人员，以开发理解和设计不同规模的在线互动的新方法。该研究旨在确定新形式的子结构出现的规模的集团增加到中型规模和全球规模;确定新的方式来保持一个有效的流动的想法和过程达成共识时，参与者的人数在讨论中增长显着ficantly;当人们可以在可识别性和匿名性之间参与时，开发技术来解决冲突和两极分化的可能性增加的问题。该项目团队结合了多个研究视角，在分析社交互动数据和开发社交网络动态模型方面具有广泛的专业知识;意见提取，总结和论点挖掘;建模会话行为;语言语用学的大规模数据分析，包括说服和信息传播;以及计算机介导的通信，社交计算和人机交互。除了基本的研究问题，该项目还寻求提供新的知识，建立中型平台的公司和组织可以采用，以最大限度地提高其网站的有效性;通过发布数据集，代码和工作应用程序来增加研究基础设施;通过研究生和本科生的跨学科培训来影响教育，包括来自代表性不足群体的学生;更广泛地说，是创造一种机制，使在线互动更有成效。

The Internet has enabled new kinds of interaction among people at a wide variety of scales, ranging from small groups to global social networks. The data arising from these new forms of interaction have been challenging to integrate because of how they span this wide range of scales; yet without this integration, we cannot understand how micro-level group interactions build up to macro-scale behavior or how macro-level factors shape small group interaction. A key data-science challenge is to bridge this gap between the micro- and macro-levels; and a crucial but relatively unexplored part of this challenge resides in the lack of conceptually useful models and techniques at the intermediate scales in between. This project investigates online social interaction data at different levels of scale, and develops methods for bridging the extremes by taking into account not just the global scales but also the intermediate meso-scale interaction (constituting interactions among hundreds or thousands of participants). Developing methods for handling the meso-level of data not only poses scientifically rich questions in its own right but also allows for transformative theory building across micro- and macro-level phenomena. The project brings together researchers from a wide range of backgrounds to develop new ways of understanding and designing online interaction across different levels of scale. The research seeks to identify new forms of sub-structure that arise as the scale of the group increases to meso-scale and on to global scale; to identify new ways of maintaining an effective flow of ideas and processes for reaching consensus when the number of participants in a discussion grows significantly; and to develop techniques for addressing the increased potential for conflict and polarization when people can participate at a boundary between recognizability and anonymity. The project team combines multiple research perspectives, with broad expertise in analyzing social interaction data and developing models of social network dynamics; opinion extraction, summarization and argument mining; modeling conversational behavior; large-scale data analysis of pragmatics of language, including persuasion and information spread; and computer-mediated communication, social computing and human-computer interaction. In addition to the underlying research questions, the project also seeks to provide new knowledge that companies and organizations building meso-scale platforms can adopt to maximize the effectiveness of their sites; to add to research infrastructure through the release of datasets, code, and working applications; to impact education through the interdisciplinary training of graduate and undergraduate students, including students from underrepresented groups; and more generally to create mechanisms that can make online interactions more productive.