Search (3 results, page 1 of 1)

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Abstract

In a digital library, book search is one of the most important information services. However, with the rapid development of network technologies such as cloud computing, the server-side of a digital library is becoming more and more untrusted; thus, how to prevent the disclosure of users' book query privacy is causing people's increasingly extensive concern. In this article, we propose to construct a group of plausible fake queries for each user book query to cover up the sensitive subjects behind users' queries. First, we propose a basic framework for the privacy protection in book search, which requires no change to the book search algorithm running on the server-side, and no compromise to the accuracy of book search. Second, we present a privacy protection model for book search to formulate the constraints that ideal fake queries should satisfy, that is, (i) the feature similarity, which measures the confusion effect of fake queries on users' queries, and (ii) the privacy exposure, which measures the cover-up effect of fake queries on users' sensitive subjects. Third, we discuss the algorithm implementation for the privacy model. Finally, the effectiveness of our approach is demonstrated by theoretical analysis and experimental evaluation.

Date

6. 1.2020 17:22:25

Type

a

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Abstract

As a healthcare ICT4D solution, mobile health (mHealth) can potentially improve users' well-being during pandemics, especially in developing countries with limited healthcare resources. Recent ICT4D research reveals that providing end-users with access to ICT is insufficient for improving well-being and, thus, understanding how mHealth empowers end-users to enhance well-being against stressful events is important. However, prior research has rarely discussed the issue of empowerment in the domain of mHealth or the context of major disruptive events. This paper contributes to the literature by conceptualizing the psychological empowerment of mHealth users (PEMU) and investigating its nomological network during pandemics. Drawing upon theories of psychological empowerment and event characteristics, we developed a research model and tested it through a mixed-methods investigation, containing a quantitative study with 602 Chinese mHealth users during COVID-19 and a follow-up qualitative study of 326 online articles and reviews. We found that PEMU, driven by three technological characteristics (perceived response efficacy, ease of use, and mHealth quality), affects well-being through both (a) a stress-buffering effect, which counterbalances the detrimental, stress-increasing effects of event criticality and disruption, and (b) a vitality-stimulating effect, which is intensified by event criticality. These findings have important implications for ICT4D research and practice.

Type

a

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Abstract

Bullet screen comments (BSCs) are user-generated short comments that appear as real-time overlays on many video platforms, expressing the audience opinions and emotions about different aspects of the ongoing video. Unlike traditional long comments after a show, BSCs are often incomplete, ambiguous in context, and correlated over time. Current studies in sentiment analysis of BSCs rarely address these challenges, motivating us to develop an aspect-level sentiment analysis framework. Our framework, BSCNET, is a pre-trained language encoder-based deep neural classifier designed to enhance semantic understanding. A novel neighbor context construction method is proposed to uncover latent contextual correlation among BSCs over time, and we also incorporate semi-supervised learning to reduce labeling costs. The framework increases F1 (Macro) and accuracy by up to 10% and 10.2%, respectively. Additionally, we have developed two novel downstream tasks. The first is noisy BSCs identification, which reached F1 (Macro) and accuracy of 90.1% and 98.3%, respectively, through fine-tuning the BSCNET. The second is the prediction of future episode popularity, where the MAPE is reduced by 11%-19.0% when incorporating sentiment features. Overall, this study provides a methodology reference for aspect-level sentiment analysis of BSCs and highlights its potential for viewing experience or forthcoming content optimization.

Type

a