III: EAGER: Discovering Spontaneous Social Events

真实世界的社交活动提供了一种方便直观的方式来组织个人的社交媒体内容。从社交媒体进行事件检测的当前方法：假设要监视的事件（及其社交媒体签名）是先验已知的;主要关注文本数据，并且未能利用其他形式的媒体，例如，图像.在此背景下，该项目探索了一种新的方法来发现自发的，先验未指定的，社会事件，通过联合贝叶斯非参数建模的多模态数据（包括文本和图像），并使用由此发现的事件，以促进新的社会联系。由此产生的事件发现工具将在一个应用程序中进行测试，该应用程序涉及从twitter短信和个人发布的图像中发现野生动物疾病爆发。该项目汇集了一个跨学科的研究人员团队，他们具有图像分析，文本挖掘和机器学习方面的专业知识，以推进从社交媒体数据中检测自发，先验未指定事件（当它们出现时）的最新技术。它有望产生新的可扩展的非参数贝叶斯方法，以联合建模的图像和文本数据，更普遍的多模态社交媒体数据。由此产生的工具可能会改变人们使用社交媒体数据的方式，使他们能够发现和参与真实的世界事件，即使它们出现。

Real-world social events provide a convenient and intuitive way to organize social media content for individuals. Current approaches to event detection from social media: assume that the events to be monitored (and their social media signatures) are known a priori; focus largely on text data and fail to take advantage of other forms of media e.g., images. Against this background, this project explores a novel approach to discovering spontaneous, a priori unspecified, social events through joint Bayesian non parametric modeling of multi-modal data (including text and images) and using the events thus discovered to foster new social links. The resulting tools for event discovery will be tested in an application involving discovery of wild animal disease outbreaks from twitter text messages and images posted by individuals. The project brings together an interdisciplinary team of researchers with expertise in image analysis, text mining, and machine learning to advance the state of the art in detection of spontaneous, a priori unspecified events (as they emerge) from social media data. It is expected to yield new scalable nonparametric Bayesian approaches to joint modeling of image and text data, and more generally multi-modal social media data. The resulting tools could potentially transform the way in which people use social media data by empowering them to discover and participate in real world events even as they emerge.