This book systematically explores how to build reliable, cross-platform multimodal event detection systems that operate under open-world conditions in today's complex social media environment. It delves into three core challenges: information fragmentation caused by incomplete individual posts; cross-platform heterogeneity arising from the diverse ways different platforms (e.g., Twitter, Flickr) represent events; and the challenge of open-world discovery driven by the constant emergence of new event types. This book not only reveals how these challenges interact to constrain traditional methods but also provides specialized, cutting-edge solutions for each. To address these challenges, this book proposes a progressive methodological framework consisting of three specialized models (MFEK, SSMC, and DAEO). By integrating external knowledge, employing self-supervised learning, and using uncertainty-aware new category discovery mechanisms, this framework offers a comprehensive guide from theory to practice. Furthermore, the book contributes several purpose-built datasets (SED, CSED, and MSED) designed to simulate real-world scenarios, aiming to foster reproducible research and bridge the gap between laboratory results and practical deployment. This work serves as a key reference for researchers and practitioners seeking to understand and build the next generation of intelligent event monitoring systems. This book targets researchers, graduate students in the fields of Artificial Intelligence, Natural Language Processing (NLP), Multimedia Computing, and Data Mining, who are focusing on multimodal analysis and social media analytics. Data scientists, software engineers, and industry practitioners working on crisis management systems, public opinion monitoring, misinformation detection, and real-time news aggregation services will also find this book to be a valuable resource.
