Enhancing local live tweet stream to detect news Twitter captures invaluable information about realworld news, spanning a wide scale from large nationalinternational stories like a presidential election to small local stories such as a local farmers market. Detecting and extracting small news for a local place is a challenging problem and the focus of this work. The main challenge lies in identifying these small stories that correspond to a local area of interest, which are typically harder to detect compared to national stories in the sense that there may be just a handful of tweets about a local story. A system, called Firefly, is proposed that overcomes the data sparsity and captures thousands of local stories per day from a metropolitan area (e.g., Boston). The key idea lies in combining the enhancement of a local live tweet stream in Twitter, the identification of xe2x80x9clocalityawarexe2x80x9d keywords, and using these keywords to cluster tweets. Experiments show that the proposed system has a significantly higher recall over a set of representative local news agencies, and at the same time, outperforms the baseline approach TwitterStand. More importantly, the results also demonstrate that our system, by utilizing the enhanced local live tweet stream, discovers much more local news than the methods working only on geotagged tweets, i.e., those with embedded GPS coordinate values.

Jointly Embedding the Local and Global Relations of Heterogeneous Graph for Rumor Detection The development of social media has revolutionized the way people communicate, share information and make decisions, but it also provides an ideal platform for publishing and spreading rumors. Existing rumor detection methods focus on finding clues from text content, user profiles, and propagation patterns. However, the local semantic relation and global structural information in the message propagation graph have not been well utilized by previous works. In this paper, we present a novel globallocal attention network (GLAN) for rumor detection, which jointly encodes the local semantic and global structural information. We first generate a better integrated representation for each source tweet by fusing the semantic information of related retweets with the attention mechanism. Then, we model the global relationships among all source tweets, retweets, and users as a heterogeneous graph to capture the rich structural information for rumor detection. We conduct experiments on three realworld datasets, and the results demonstrate that GLAN significantly outperforms the stateoftheart models in both rumor detection and early detection scenarios.