Collaborative Research: Discovering and Exploiting Latent Communities in Social Media

尽管社交网络平台和应用程序在当今日常生活中普遍存在，但现有的社交媒体研究将术语“社交”和“媒体”分开，未能解决智能管理和利用社交媒体的重要需求，例如找到用户想要的信息，将信息置于赋予其意义的社交背景中，并为错综复杂的关系网络提供秩序和结构。这个跨学科的项目将通过将社会智能语言处理与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。具体而言，该项目将：（a）发现社会语言社区，并确定社区成员构成的人口和社会学因素;（B）发现不同级别的跨社区语言差异，并为方言计量和社会语言分析以及预测用户兴趣和趋势开发新的计算工具;及（c）推荐内容及跨越社区界限的社交关系，这将有助人们透过新资讯、意见及社交关系扩阔视野。该项目将导致（a）新的建模形式主义，共同将语言信息与社会网络元数据;（B）一个新的计算方法，从原始文本的社会语言学调查;以及（c）灵活的语言变化模型，其对时间动态进行建模并且超越简单的词袋方法而移动到诸如多词表达之类的高阶现象，语法和联合拼写变化。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，例如智能推理社区结构和语言模式以及社交媒体中的惯例的软件。研究结果将有利于广泛的需求，如个性化的信息服务和情报和安全业务，这需要准确和及时地了解社会文化事件和趋势。该项目还将提供本科生的研究机会，并通过暑期项目向高中生推广。

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.