Collaborative Research: RI: Processing Opinion Sharing Dialogue in Social Media

当人们做出决定或形成信念时，他们经常与他人讨论，征求他人的意见，并分享自己的意见。最近，这样的对话正在网上进行，为公司、军方、政府、公共政策机构和教育工作者提供了感兴趣的公共信息来源。此外，这些对话是大规模发生的，使自然语言处理的研究人员第一次能够访问大规模对话数据集。然而，自动处理这样的对话是具有挑战性的，因为当前的工具是针对传统语言资源的，例如报纸文章。该项目开发了自动处理和识别这类对话中重要现象的创新算法，包括：(A)立场--参与者对主题的看法；(B)主观对话行为--包括讽刺和幽默；(C)中心命题--对话中的核心思想，通过结合众包注释、自举和机器学习以及认知科学的方法。一个重要的项目输出是一个新的语料库，包括注释和对话摘要。更长期的影响包括公共政策，为政府和军方提供方法，以发现“街上的人”对当前话题的看法。教育工作者可以重新使用语料库和工具，让孩子们接触到关于重要问题的令人信服的论点。对意见分享对话的更好理解为新的认知实验和理论提供了可能：自动识别令人信服的论点使政治学和社会心理学研究人员能够检查学习和意见形成。该项目培训本科生和研究生进行跨学科研究，结合社交媒体、人机交互、计算语言学和自然语言处理。

When people make decisions or form beliefs, they often discuss them with others, seeking out others' opinions and sharing their own. Recently such conversations are occurring online, providing a public source of information of interest to companies, the military, the government, public policy bodies, and educators. Moreover these dialogs occur at scale, allowing researchers in natural language processing access to large-scale dialog datasets for the first time. However, automatically processing such dialogs is challenging, because current tools are targeted to traditional language resources such as newspaper articles. This project develops innovative algorithms for automatically processing and identifying important phenomena in such dialogs including: (a) stance - participants' views on a topic; (b) subjective dialog acts - including sarcasm and humor; and (c) central propositions - core ideas in the dialog, by combining methods of crowd-sourced annotation, bootstrapping and machine learning, and cognitive science. A critical project output is a new corpus, including annotations and dialogic summaries. Longer term impacts include public policy, providing government and the military with methods to discover what "the man on the street" is saying about current topics. Educators can re-use the corpora and tools to expose children to compelling arguments about important issues. Greater understanding of opinion sharing dialog enables new cognitive experiments and theory: automatically identifying compelling arguments allows political science and social psychology researchers to examine learning and opinion formation. The project trains undergraduate and graduate students in interdisciplinary research combining social media, human computer interaction, computational linguistics and natural language processing.