Metadata Connector: Exploiting Hashtag and Tag for Cross-OSN Event Search

作者：Yuqi Gao, Jitao Sang, Chengpeng Fu, Zhengjia Wang, Tongwei Ren, Changsheng Xu · 发表于：IEEE Transactions on Multimedia · 年份：2020 · DOI：10.1109/tmm.2020.2982047 · 被引用次数：3 · 研究领域：Complex Network Analysis Techniques、Web Data Mining and Analysis、Advanced Text Analysis Techniques

Social media has revolutionized the way people understand and keep track of real-world events. Various related multimedia information in different modalities such as texts, images and videos is updated on social media and reflects the events. These quantities of information distributes on different Online Social Networks (OSNs), which provides rich, wide coverage, comprehensive information about the trending events. Faced with such large amounts of data, searching has become a handy tool for event understanding and tracking on social media. However, existing single-OSN search mainly involves with single modality on single platform. Moreover, most OSNs usually focus on biased perspective of events, which significantly limits the coverage and diversity of single-OSN based event search. In this paper, we introduce a novel cross-OSN framework to help integrate these cross-OSN information regarding the same event and provide an immersive experience for information retrieval. Since social media information is widely distributed in different OSNs where semantic gap exists among these heterogeneous spaces, we propose to utilize hashtag and tag, which are user-generated metadata for organizing and labeling in many OSNs, as bridges to connect between different OSNs. In our four-stage solution framework, various methods are adopted for hashtag and tag filtering, search results representation, clustering and demonstration. Given an event query, in the first stage we generate related item...