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Abstract

Public health surveillance based on data analytics plays a crucial role in detecting and responding to public health crises, such as infectious disease outbreaks. Previous information science research on the topic has focused on developing analytical algorithms and visualization tools. This study seeks to extend the research by investigating information practices in data analytics for public health surveillance. Through a case study of how data analytics was conducted for surveilling Influenza A and COVID-19 outbreaks, both exploration information practices (i.e., probing, synthesizing, exchanging) and exploitation information practices (i.e., scavenging, adapting, outreaching) were identified and detailed. These findings enrich our empirical understanding of how data analytics can be implemented to support public health surveillance.

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Abstract

Zhou and Zhang believe that for the potential of natural language processing NLP to be reached in information retrieval a framework for guiding the effort should be in place. They provide a graphic model that identifies different levels of natural language processing effort during the query, document matching process. A direct matching approach uses little NLP, an expansion approach with thesauri, little more, but an extraction approach will often use a variety of NLP techniques, as well as statistical methods. A transformation approach which creates intermediate representations of documents and queries is a step higher in NLP usage, and a uniform approach, which relies on a body of knowledge beyond that of the documents and queries to provide inference and sense making prior to matching would require a maximum NPL effort.

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Abstract

The emergence of information portal systems in the past few years has led to a greatly enhanced Web-based environment for users seeking information online. While considerable research has been conducted an user information-seeking behavior in regular IR environments over the past decade, this paper focuses specifically an how users in a medical science and clinical setting carry out their daily information seeking through a customizable information portal system (MyWelch). We describe our initial study an analyzing Web usage data from MyWelch to see whether the results conform to the features and patterns established in current information-seeking models, present several observations regarding user information-seeking behavior in a portal environment, outline possible long-term user information-seeking patterns based an usage data, and discuss the direction of future research an user information-seeking behavior in the MyWelch portal environment.

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Abstract

Accurate demand forecasting plays a critical role in hotel revenue management. Online reviews have emerged as a viable information source for hotel demand forecasting. However, existing hotel demand forecasting studies leverage only sentiment information from online reviews, leading to capturing insufficient information. Furthermore, prevailing hotel demand forecasting methods either lack explainability or fail to capture local correlations within sequences. In this study, we (1) propose a comprehensive framework consisting of four components: expertise, sentiment, popularity, and novelty (ESPN framework), to investigate the impact of online reviews on hotel demand forecasting; (2) propose a novel dual attention-based long short-term memory convolutional neural network (DA-LSTM-CNN) model to optimize the model effectiveness. We collected online review data from Ctrip.com to evaluate our proposed ESPN framework and DA-LSTM-CNN model. The empirical results show that incorporating features derived from the ESPN improves forecasting accuracy and our DA-LSTM-CNN significantly outperforms the state-of-the-art models. Further, we use a case study to illustrate the explainability of the DA-LSTM-CNN, which could guide future setups for hotel demand forecasting systems. We discuss how stakeholders can benefit from our proposed ESPN framework and DA-LSTM-CNN model.