The rapid development of the internet has ushered the real world into a “media-centric” digital era where virtually everything serves as a medium. Leveraging the new attributes of interactivity, immediacy, and personalization facilitated by online communication, folklore has found a broad avenue for dissemination. Among these, online social networks have become a vital channel for propagating folklore. By using social network theory, we devise a comprehensive approach known as SocialPre. Firstly, we utilize embedding techniques to capture users’ low-level and high-level social relationships. Secondly, by applying an automatic weight assignment mechanism based on the embedding representations, multi-level social relationships are aggregated to assess the likelihood of a social interaction between any two users. These experiments demonstrate the ability to classify different social groups. In addition, we delve into the potential directions of folklore evolution, thus laying a theoretical foundation for future folklore communication.

Air pollution is a severe environmental problem in urban areas. Accurate air quality prediction can help governments and individuals make proper decisions to cope with potential air pollution. As a classic time series forecasting model, the AutoRegressive Integrated Moving Average (ARIMA) has been widely adopted in air quality prediction. However, because of the volatility of air quality and the lack of additional context information, i.e., the spatial relationships among monitor stations, traditional ARIMA models suffer from unstable prediction performance. Though some deep networks can achieve higher accuracy, a mass of training data, heavy computing, and time cost are required. In this paper, we propose a hybrid model to simultaneously predict seven air pollution indicators from multiple monitoring stations. The proposed model consists of three components: (1) an extended ARIMA to predict matrix series of multiple air quality indicators from several adjacent monitoring stations; (2) the Empirical Mode Decomposition (EMD) to decompose the air quality time series data into multiple smooth sub-series; and (3) the truncated Singular Value Decomposition (SVD) to compress and denoise the expanded matrix. Experimental results on the public dataset show that our proposed model outperforms the state-of-art air quality forecasting models in both accuracy and time cost.

With the ever-increasing number of natural disasters warning documents in document databases, the document database is becoming an economic and efficient way for enterprise staffs to learn and understand the contents of the natural disasters warning through searching for necessary text documents. Generally, the document database can recommend a mass of documents to the enterprise staffs through analyzing the enterprise staff’s precisely typed keywords. In fact, these recommended documents place a heavy burden on the enterprise staffs to learn and select as the enterprise staffs have little background knowledge about the contents of the natural disasters warning. Thus, the enterprise staffs fail to retrieve and select appropriate documents to achieve their desired goals. Considering the above drawbacks, in this paper, we propose a fuzzy keywords-driven Natural Disasters Warning Documents retrieval approach (named NDWD_keyword). Through the text description mining of documents and the fuzzy keywords searching technology, the retrieval approach can precisely capture the enterprise staffs’ target requirements and then return necessary documents to the enterprise staffs. Finally, a case study is run to explain our retrieval approach step by step and demonstrate the effectiveness and feasibility of our proposal.