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

BIGDATA：IA：在多个尺度上利用语言和交互动态来最大化群体交互的好处

• 批准号: 1741441
• 负责人: Jon Kleinberg
• 金额: $ 98.35万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1741441&HistoricalAwards=false
• 关键词: BIGDATA; IA; Harnessing; Language; Interaction

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.

从小团体到全球社交网络，互联网使人与人之间在各种不同规模的新型互动成为可能。这些新的互动形式产生的数据很难整合，因为它们跨越了如此广泛的范围；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层面的行为，或者宏观层面的因素如何塑造小群体互动。一个关键的数据科学挑战是弥合微观和宏观之间的这一差距；这一挑战的一个关键但相对未被探索的部分在于，在两者之间的中间尺度上缺乏概念上有用的模型和技术。该项目调查了不同规模的在线社会互动数据，并开发了弥合极端的方法，不仅考虑了全球规模，还考虑了中间中尺度的相互作用(构成数百或数千参与者之间的相互作用)。开发处理中观层面数据的方法不仅本身提出了丰富的科学问题，而且还允许建立跨越微观和宏观层面现象的变革性理论。该项目汇集了来自广泛背景的研究人员，以开发新的方式来理解和设计不同规模的在线互动。这项研究试图确定随着集团规模扩大到中观规模和全球规模而出现的新的次级结构形式；确定在讨论参与者人数显著增加时保持有效思想流动和达成共识的新方法；以及开发技术来解决当人们可以在可认知性和匿名性之间参与时增加的冲突和两极分化的可能性。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；意见提取、总结和论点挖掘；对话行为建模；语言语用学的大规模数据分析，包括说服和信息传播；以及计算机中介沟通、社会计算和人机交互方面拥有广泛的专业知识。除了基本的研究问题，该项目还寻求提供新的知识，供构建中尺度平台的公司和组织采用，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；通过对研究生和本科生(包括来自代表性不足群体的学生)的跨学科培训来影响教育；以及更广泛地说，创建能够使在线互动更有成效的机制。

项目成果

Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives

• DOI: 10.18653/v1/n19-1052
• 发表时间: 2019-04
• 作者: Liye Fu;Jonathan P. Chang;Cristian Danescu-Niculescu-Mizil

Finding Your Voice: The Linguistic Development of Mental Health Counselors

• DOI: 10.18653/v1/p19-1089
• 发表时间: 2019-06
• 期刊: ArXiv
• 作者: Justine Zhang;Robert Filbin;C. Morrison;Jaclyn Weiser;Cristian Danescu-Niculescu-Mizil

Characterizing Online Public Discussions through Patterns of Participant Interactions

通过参与者互动的模式来表征在线公共讨论

• DOI: 10.1145/3274467
• 发表时间: 2018
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Zhang, Justine;Danescu-Niculescu-Mizil, Cristian;Sauper, Christina;Taylor, Sean J.
• 通讯作者: Taylor, Sean J.

Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets

• DOI: 10.18653/v1/n18-1199
• 发表时间: 2018-04
• 期刊: ArXiv
• 作者: Jack Hessel;David Mimno;Lillian Lee

Persuasion of the Undecided: Language vs. the Listener

• DOI: 10.18653/v1/w19-4519
• 发表时间: 2019-08
• 作者: Liane Longpre;Esin Durmus;Claire Cardie