In this paper, we focus on event discovery by utilizing data distributed in multiple media domains, such as news media and social media. To this end, we propose an in-domain and cross-domain Laplacian regularization (ICLR) model, which can learn effective data representation for both textual news reports contributed by journalists in news media domain, and image posts shared by amateur users in social media domain. The achieved data representation can be used by classification and clustering strategies for existing and new event discovery, respectively. More specifically, ICLR constructs respective Laplacian regularization terms considering the property of inter-domain and intra-domain label consistency, which can be optimized by employing an alternating optimization strategy with theoretical guarantee for convergence. In particular, we collect and release a multi-domain and multimodal dataset for evaluations and public use.
Bibliographical noteThis work is supported by the National Natural Science Foundation of China (No. 61703109 ), the Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515010616), the Guangdong Innovative Research Team Program (No. 2014ZT05G157), the Research Grants Council of the Hong Kong Special Administrative Region, China (Collaborative Research Fund, project number C1031-18G), the Key-Area Research and Development Program of Guangdong Province (2019B010136001), the Science and Technology Planning Project of Guangdong Province LZC0023, the HKIBS Research Seed Fund 2019/20 (190-009), and the Research Seed Fund (102367) of Lingnan University, Hong Kong.
- Data representation learning
- Event detection
- Social media
- Multi-modality data