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演讲简介
Social media has become a popular data source as a surrogate for monitoring and detecting events. Analyzing social media (e.g., tweets) to reveal event information requires sophisticated techniques. Tweets are written in unstructured language and often contain typos, non-standard acronyms, and spam. In addition to the textual content, Twitter data form a heterogeneous information network where users, tweets, and hashtags have mutual relationships. These features pose technical challenges for designing event detection and forecasting methods. In this talk, I will present the design and implementation of a fully automated forecasting system for significant societal events using open-source data including tweets, blog posts, and news articles. I will describe the system architecture, individual models that leverage specific data sources, and a fusion engine that supports the trade-off between specific evaluation criteria. I will also demonstrate its superiority and capability to forecast significant societal happenings.
关于讲者
吕昌田，美国弗吉尼亚理工大学正教授，大华府校区计算机系主任兼工程学院指导委员会主席，人工智能与数据分析中心副主任。2001获美国明尼苏达大学双子城校区博士学位，曾担任人工智能工具国际会议主席，地理信息系统国际会议主席，空间和时间数据库研讨会主席，国际计算机协会空间信息组副主席。目前主要从事大数据，数据挖掘，人工智能，城市计算，地理数据库，与智能型交通系统等多方面的前端研究。在国际一流高水平会议期刊已发表200多篇研究论文。目前担任地理信息学，数据与知识工程，空间算法与系统，与信息科学与工程等著名国际期刊副主编。研究工作长期获得美国国防部，美国国家科学基金会，美国国家卫生研究院，情报高等研究计划署，弗吉尼亚州交通局，华盛顿都会区交通局，与哥伦比亚特区交通局等研发机构奖励资助。2015获评美国计算机学会杰出科学家。
