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

III：小：通过基于相似性和基于图的属性和查询推断来社交发现社交媒体中的用户和内容

• 批准号: 1619302
• 负责人: Kevin Chang
• 金额: $ 50万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619302&HistoricalAwards=false
• 关键词: III; Small; Social; Discovery; Users

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.

该项目旨在开发基于图表的工具，以发现社交媒体中与给定问题或目标最相关的人和内容。对于包括营销、事件检测、公民参与和治理以及灾难管理在内的应用程序来说，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，但没有足够的关于他们的属性和态度的信息，无法做出正确的选择，听谁的。研究小组建议使用相似性或广义的“同源”原则来标记人和内容，即人们彼此之间越接近(比如朋友、邻居或推特追随者)，他们就越有可能有共同之处。研究团队将开发SocialSense，这是一种工具，它利用这种想法，根据人们与邻居的联系方式来猜测他们的标签和内容；这些推断出的标签将允许用户在社交媒体上创建更复杂、更准确的查询。该团队将与现有合作伙伴合作开发SocialSense，并验证它比目前的社交媒体工具做得更好；他们还将使用该工具支持围绕网络搜索和数据库的本科生和研究生课程。对于寻找用户的问题，团队将用户和内容表示为大型社交图谱中的节点，其中每个节点和它们之间的边缘都有一组人口统计和态度属性。该项目开发了基于图的社会关系图和内容图学习/挖掘的新算法。通过挖掘网络中的连接模式，该团队将识别一组同源的结构基元，并使用这些基元以及属性出现的潜在概率来将关于这些属性的推理传播到其他节点，并使用拒绝抽样技术检查这些推理的质量和公平性。为了查找内容，团队将在图形中表示内容和查询，并再次挖掘常见模式，这一次是为了创建查询模板，该模板将支持在新主题和实体出现时创建未来的查询。最后，团队将集成这些组件，创建一个支持跨人员和内容进行查询的系统，并基于发现与上述主题和模板匹配的关联属性的模式来建议有趣的新查询。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，评估系统的可用性、表现力和简单性，并与其机构运营的智能国家/公民输入项目和社交地图云服务建立合作伙伴关系。