Abstract
With the rapid development of mobile Internet technology, various public events appear on social media platforms and attract a lot of attention. Since netizens can express their opinions freely on the Internet, some events that cause negative public opinion seriously threaten public security, which are called Internet Public Safety events (IPSe). Existing solutions use a single metric to realize event detection, which has a high false detection rate for Internet Public Safety events. In addition, they lack countermeasures after the outbreak of public opinion, resulting in inefficient information management. This paper proposes a novel Internet Public Safety event grading method based on multi-feature, which measures events from the three perspectives of heat, emotion, and sensitivity. In order to improve the retrieval efficiency of events information, we implement a smart dynamic hot/cold data migration mechanism using a hybrid storage system containing solid-state drives and hard-disk drives, which realizes real-time data adjustment in the storage layer and ensures efficient events management. In the experiments, we verify our method through several real internet events, and the results show that our method achieves the state-of-the-art accuracy in the detection of Internet Public Safety events and realizes efficient retrieval with a low query overhead.
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Acknowledgements
This work was supported in part by the National Science Foundation of China under Grant No. 61972449, U1705261, and 61821003, and the Fundamental Research Funds for the Central Universities HUST under Grant No. 2021JYCXJJ049.
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Hu, D., Xie, Y., Feng, D., Zhao, S., Fu, P. (2023). Internet Public Safety Event Grading and Hybrid Storage Based on Multi-feature Fusion for Social Media Texts. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13943. Springer, Cham. https://doi.org/10.1007/978-3-031-30637-2_38
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DOI: https://doi.org/10.1007/978-3-031-30637-2_38
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