III: Small: Using Location for Retrieving Text and Images in News And Social Media Posts

该项目的目标是增强利用地图界面访问新闻和社交媒体帖子等文档的系统。 大多数地理搜索系统要求用户输入包围感兴趣区域的边界框的两个角的纬度和经度。 有些允许通过名称指定位置，但与名称相关联的区域是预先固定的，并且通常与用户打算搜索的区域不同。 例如，用户可以输入位置名称“洛杉矶”，希望搜索来自该城市的社交媒体帖子。 然而，系统可能被设计为将“洛杉矶”解释为表示更大的洛杉矶区域（太大）或城市的地理点中心（太小）。 这个项目将创建一个系统，克服这些问题的文本地理查询。 底层文本数据通过地图查询接口访问，使用诸如平移和缩放的直接操纵动作来导航数据。这些动作的优点是指向位置的动作（例如，通过指示设备的适当定位）并且根据缩放级别来解释该定位规范的精度等同于允许空间同义词。这意味着，当搜索“曼哈顿的摇滚音乐会”时，当在哈莱姆没有找到音乐会时，诸如哈莱姆、纽约市和布鲁克林的响应都是可接受的。由于哈莱姆包含在曼哈顿中，纽约市包括曼哈顿，而布鲁克林是曼哈顿的兄弟（邻居），因此通过使用空间同义词扩展了对哈莱姆的搜索。基于位置的地图为在本地或全球层面上阅读和分析新闻和社交媒体提供了一种新的范式。该项目解决了以下方面的挑战：（1）检测有关本地事件的推文或其他社交媒体帖子。这是困难的，因为与许多人发布推文的全球事件相比，只有少数人可能发布相关推文，从而使得更容易检测全球事件。(2)通过开发更合适的查准率和查全率评估指标，在使用文本指定的位置检索文档时提高对模糊位置名称的解析度。(3)借助热图，能够对可能对公共安全和健康产生影响的事件（如犯罪和疾病）在新闻和社交媒体（如Twitter）中的提及进行特定领域的跟踪。(4)允许用户指定所需的域以及通过使用范例推断它。(5)改进的方法的NewsStand系统，一个事先的贡献的调查员，使用聚类使用word 2 vec更好地利用语义比目前使用的TF-IDF。这种聚类用于实际文档及其相关图像和视频，并能够基于语义而不是颜色和纹理等局部特征来检测相似图像。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估而被认为值得支持。

The goal of this project is to enhance systems which make use of a map interface to access documents such as news and social media postings. Most geographic search systems require the user to enter the latitude and longitude of two corners of the bounding box that encloses the region of interest. Some allow locations to be specified by name, but the region associated with the name is fixed in advance and is often different from the region that the user intends to search. For example, a user might enter the location name "Los Angeles", hoping the search for social media posts from the city. However, the system might be designed to interpret "Los Angeles" to mean the greater Los Angeles area (too large) or the geographic point center of the city (too small). This project will create a system that overcomes these problems with text geographic queries. The underlying textual data is accessed via a map query interface using direct manipulation actions such as pan and zoom to navigate the data. The advantage of these actions is that the act of pointing at a location (e.g., by the appropriate positioning of a pointing device) and making the interpretation of the precision of this positioning specification dependent on the zoom level is equivalent to permitting spatial synonyms. This means that when searching for a "rock concert in Manhattan", responses such as Harlem, New York City, and Brooklyn are all acceptable when no concert is found in Harlem. The search for Harlem is expanded by using its spatial synonyms since Harlem is contained in Manhattan, New York City includes Manhattan, and Brooklyn is a sibling (neighbor) of Manhattan. The location-oriented map-based provides a new paradigm to reading and analyzing news and social media on a local level or global level. This project addresses challenges in: (1) Detecting tweets or other social media posts about local events. This is difficult as only a few people may be posting related tweets in contrast to global events where many people post tweets thereby making it easier to detect global events. (2) Improving the resolution of ambiguous location names when retrieving documents using textually-specified locations by developing more appropriate precision and recall evaluation metrics. (3) Enabling domain-specific tracking of mentions of events such as crimes and diseases in news and social media such as Twitter over time with the aid of heat maps which may have an impact on public safety and health. (4) Allowing users to specify the desired domain as well as infer it by use of exemplars. (5) Improving the method the NewsStand system, a prior contribution of the investigator, uses clustering by using word2vec which makes better use of semantics than the currently used TF-IDF. This clustering is used for the actual documents and their associated images and videos, and enables the detection of similar images based on semantics rather than local features such as color and texture.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.