III: Small: Social Discovery of Users and Content in Social Media Through Similarity-Based and Graph-Based Inference of Attributes and Queries

该项目旨在开发基于图形的工具，以发现与给定问题或目标最相关的社交媒体中值得关注的人和内容。对于市场营销、事件检测、公民参与和治理以及灾难管理等应用来说，这是一个重要的问题，也是一个难题，因为有太多的内容和太多的人，但却没有足够的信息来了解他们的属性和态度，从而无法做出正确的选择。研究小组建议使用相似性或广义的“同质性”原则来标记人和内容，即人们彼此之间越亲密（如朋友、邻居或Twitter粉丝），他们就越有可能有共同点。该研究团队将开发SocialSense工具，利用这一想法，根据人们与邻居的联系方式来猜测人们和内容的标签；这些推断标签将允许用户在社交媒体中创建更复杂、更准确的查询。该团队将与现有的合作伙伴共同开发SocialSense，并验证它比现有的社交媒体工具更好；他们还将使用该工具来支持本科生和研究生围绕网络搜索和数据库的课程。对于寻找用户的问题，该团队将用户和内容表示为大型社交图中的节点，其中每个节点以及它们之间的边缘都具有一组人口统计和态度属性。该项目开发了基于图的学习/挖掘社交图和内容图的新算法。通过挖掘网络中的连接模式，团队将识别一组同质性的结构基序，并使用这些基序，以及属性发生的潜在概率，将关于这些属性的推断传播到其他节点，并使用拒绝抽样技术检查这些推断的质量和公平性。为了查找内容，团队将在图中表示内容和查询，并再次挖掘公共模式，这一次是创建查询模板，以便在出现新主题和实体时支持创建未来的查询。最后，团队将集成这些组件，创建一个支持跨人员和跨内容查询的系统，并根据发现与上述主题和模板匹配的连接属性的模式提出有趣的新查询。该团队将通过离线后端性能测量和在线部署来评估方法和系统，在与智能国家/公民输入项目和由其机构运营的社交地图云服务合作的背景下，评估系统的可用性、表现力和简单性。

This project aims to develop graph-based tools to discover the people and content to pay attention to in social media that are most relevant to a given question or goal. This is an important problem for applications including marketing, event detection, civic participation and governance, and disaster management, and a hard problem because there is so much content and so many people but not enough information about their attributes and attitudes to make good choices about who to listen to. The research team proposes to label people and content using similarity or generalized "homophily" principle, the idea that the closer people are to each other (like friends or neighbors or Twitter followers), the more likely they are to have things in common. The research team will develop SocialSense, a tool that uses this idea to guess labels for people and content based on how they are connected to their neighbors; these inferred labels will allow users to create more complex and accurate queries in social media. The team will work with existing partners to develop SocialSense and validate that it does better than current social media tools; they will also use the tool to support undergraduate and graduate classes around web search and databases.For the problem of finding users, the team will represent users and content as nodes in a large social graph, where each of these and the edges between them has a set of demographic and attitudinal attributes. This project develops novel algorithms for graph-based learning/mining over social graphs as well as content graphs. Through mining patterns of connection in the network, the team will identify a set of structural motifs of homophily and use those motifs, as well as underlying probabilities of the occurrence of attributes, to propagate inferences about those attributes to other nodes, and check the quality and fairness of those inferences using a rejection sampling technique. For finding content, the team will represent content and queries in a graph and again mine common patterns, this time to create query templates that will support the creation of future queries as new topics and entities arise. Finally, the team will integrate these components, creating a system that supports querying across people and content and suggests interesting new queries based on discovering patterns of connected attributes that match the motifs and templates described above. The team will evaluate the methods and system through both offline back-end performance measurements and online deployments that evaluate usability, expressiveness, and simplicity of the systems in the context of their partnerships with a smart nation/citizen input project and a social mapping cloud service run by their institution.