Showing papers by "Paul Jen-Hwa Hu published in 2023"

A Deep Learning Approach for Semantic Analysis of COVID-19-Related Stigma on Social Media

Abstract: The rapid spread of the pandemic of coronavirus disease of 2019 (COVID-19) has created an unprecedented, global health disaster. During the outburst period, the paucity of knowledge and research aggravated devastating panic and fears that lead to social stigma and created serious obstacles to contain the disastrous epidemic. We propose a deep learning-based method to detect stigmatized contents on online social network (OSN) platforms in the early stage of COVID-19. Our method performs a semantic-based quantitative analysis to unveil essential spatial-temporal characteristics of COVID-19 stigmatization for timely alerts and risk mitigation. Empirical evaluations are carried out to examine our method’s predictive utilities. The visualization results of the co-occurrence network using Gephi indicate two distinct groups of stigmatized words that pertain to people in Wuhan and their dietary behaviors, respectively. Netizens’ participations and stigmatizations in the Hubei region, where the COVID-19 broke out, are twice ( $p < 0.05$ ) and four ( $p < 0.01$ ) times more frequent and intense than those in other parts of China, respectively. Also, the number of COVID-19 patients is correlated with COVID-19-related stigma significantly (correlation coefficient = 0.838, $p < 0.01$ ). The responses to individual users’ posts have the power law distribution, while posts by official media appear to attract more responses (e.g., likes, replies, and forward). Our method can help platforms and government agencies manage public health disasters through effective identification and detailed analyses of social stigma on social media.

Deep Learning-Based Imputation Method to Enhance Crowdsourced Data on Online Business Directory Platforms for Improved Services

Abstract: ABSTRACT Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, depend on voluntarily user-submitted data about various businesses to assist consumers in finding appropriate options for transactions. Yet the crowdsourced nature of such data restricts the availability of attribute values for many businesses on the platform. Crowdsourced data often suffer serious completeness and timeliness constraints, with negative implications for key stakeholders such as users, businesses, and the platform. We thus develop a novel, deep learning–based imputation method, premised in institutional theory, to estimate missing attribute values of individual businesses on an OBD platform. The proposed method leverages a deep model architecture and considers both inter-business and inter-attribute relationships for imputations. An application to a Yelp data set reveals our method’s greater imputation effectiveness relative to prevalent methods. To illustrate the method’s practical utilities and values, we further examine the efficacy of business recommendations empowered by its imputed business attribute values, in comparison with those enabled by data imputed by benchmark methods. The results affirm that the proposed method substantially outperforms benchmarks for imputing missing attribute values and empowers more effective business recommendations. This study addresses crucial, prominent completeness and timeliness constraints in crowdsourced data on OBD platforms and offers insights for downstream applications that can improve user experiences, firm performance, and platform services.