Collaborative Research: Discovering and Exploiting Latent Communities in Social Media

协作研究：发现和利用社交媒体中的潜在社区

• 批准号: 1110904
• 负责人: Scott Kiesling
• 金额: $ 20.22万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1110904&HistoricalAwards=false
• 关键词: Collaborative; Research; Discovering; Exploiting; Latent

Despite the prevalence of social network platforms and apps in nowadays daily life, existing research on social media takes the terms "social" and "media" separately, and fails to address important needs for intelligently managing and utilizing social media, such as finding the information that users want, situating information in a social context that gives it meaning, and providing order and structure to an intricate and intertwined network of relationships. This interdisciplinary project will provide a holistic view of social media by combining socially intelligent language processing with linguistically motivated social network analysis. Specifically, the project will: (a) discover sociolinguistic communities and identify the demographic and sociological factors that underlie community membership; (b) discover cross-community linguistic variation at various levels and develop new computational tools for dialectometric and sociolinguistic analysis and for prediction of user interests and trends; and (c) recommend content and social connections across community boundaries, which will help people to broaden their perspectives with new information, opinions, and social relationships.Intellectual merit: The project will lead to (a) new modeling formalisms that jointly incorporate linguistic information with social network metadata; (b) a new computational methodology for sociolinguistic investigation from raw text; and (c) flexible models of linguistic variation that model temporal dynamics and move beyond simplistic bag-of-words approaches to higher-order phenomena such as multi-word expressions, syntax, and joint orthographic variation. Broader impacts: The project will lead to advancements in basic research in statistical machine learning, social sciences, and language technology. It will also bring innovations and practical applications in all these areas, such as software that reasons intelligently about community structures and linguistic patterns and conventions in social media. The findings will benefit a wide range of needs, such as personalized information service and intelligence and security operations, which require precise and timely understanding of social-cultural events and trends. The project will also provide undergraduate research opportunities and outreach to high school students through summer programs.

尽管社交网络平台和应用程序在当今的日常生活中非常普遍，但现有的社交媒体研究将“社交”和“媒体”这两个术语分开，未能解决智能管理和利用社交媒体的重要需求，如找到用户想要的信息，将信息置于赋予其意义的社交上下文中，以及为错综复杂和相互交织的关系网络提供秩序和结构。这个跨学科项目将通过将社交智能语言处理与出于语言动机的社交网络分析相结合，提供对社交媒体的整体看法。具体地说，该项目将：(A)发现社会语言社区，确定社区成员背后的人口和社会学因素；(B)发现不同级别的跨社区语言差异，并开发新的计算工具，用于方言和社会语言分析以及预测用户兴趣和趋势；(C)推荐跨越社区边界的内容和社会关系，这将帮助人们用新的信息、意见和社会关系拓宽他们的视角。以及(C)灵活的语言变异模型，模拟时间动态，超越简单的词袋方法，研究更高层次的现象，如多词表达、句法和联合拼写变异。更广泛的影响：该项目将促进统计机器学习、社会科学和语言技术方面的基础研究。它还将在所有这些领域带来创新和实际应用，例如能够智能地推理社区结构以及社交媒体中的语言模式和惯例的软件。调查结果将有利于广泛的需求，如个性化信息服务以及情报和安全行动，这些需要准确和及时地了解社会文化事件和趋势。该项目还将通过暑期项目向高中生提供本科生研究机会和外展服务。