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The purpose of this study is to investigate the utilitarian value (UV), hedonic value (HV) and social value (SV) that make people use a certain type of online media website and how the usage of specific online media website impact the way people perceive online information credibility (OIC). A research model was also proposed to explain the essence of this study.

This study adopted the survey research methodology to empirically test the research model with 873 research participants from the University of Science and Technology of China and Anhui Medical University.

Results from structural equation modeling showed that UV and HV have a significant positive impact on the usage of print news media website (PNMW), usage of broadcast news media website (BNMW) and usage of social networking website (SNW). The SV was also found to have a significant positive impact on the usage of SNWs. The result also indicated that the usage of the PNMW and the usage of the BNMW by online users have a significantly positive impact on high rating of OIC. However, the result showed that the usage of SNW does not have a significant positive impact on the high rating of OIC.

Findings in this study provided substantial contributions toward the advancement of the uses and gratification theoretical framework by unraveling how certain motivational values can influence online media users’ preferences for specific online media websites, as well as showing how specific online media websites affect online users’ perception of OIC.

The purpose of this paper is to investigate the impact of e-government usage on citizen engagement during the COVID-19 crisis in China, in relation to the mediating role of how citizens perceive the government. A model was also proposed to explain the relationship between e-government usage during the COVID-19 crisis and the mediating role that different perceptions of government play in influencing citizens level of engagement.

The research model was tested empirically through a survey conducted online with 866 research participants, comprising of Chinese citizens from three large cities, which include Hefei, Shanghai and Nanjing.

The results in structural equation modeling showed that e-government usage has a significant positive influence on citizens' perception about trust in government, government transparency and government reputation but not significant influence on citizens' engagements. However, an indirect relationship was found out in the mediation analysis. There was also a significant relationship between the different perceptions of government. Mediation analysis showed that all the different perceptions of government mediate the relationship between e-government usage and citizens' engagements during the COVID-19 crisis. The single mediation pathways were found to be most effective mediators, identifying citizens' perception about trust in government to be the most effective mediator.

This study filled the gap in literature by examining how e-government usage by Chinese citizens during the COVID-19 crisis helped influence their attitude and behavior. Specifically, this study is one of the first to integrate citizens' usage of e-government and citizens' engagement through the different citizens' perceptions of government such as trust in government, transparency of government and government reputation in a non-liberal country.

The purpose of this paper is to investigate university students’ WeChat usage during the COVID-19 pandemic lockdown in relation to the mediating role of online self-disclosure on their quality of friendship and well-being. A model is proposed to explain how students’ interactions occur during the lockdown and the mediatory role which self-disclosure plays in influencing their socio-psychological markup.

The research model was tested empirically through a survey conducted online with 600 research participants, comprising of university students in China.

Results in structural equation modeling show that WeChat interaction significantly correlates with the quality of friendship, online self-disclosure but not significantly correlates with well-being, but an indirect relationship was found out in the mediation analysis. There is also a significant relationship between online self-disclosure, quality of friendship and well-being. Mediation analysis shows that online self-disclosure mediates the relationship between interactions on WeChat and quality of friendship; it also mediates the relationship between WeChat interaction and well-being. In all, the results achieved in this study will significantly help provide more insights in comprehending the nuances attached to some socio-psychological aspects of WeChat and how its usage affects people during the period of crisis.

Theoretically based investigation of WeChat usage among university students and its relationship with online self-disclosure, quality of friendship and well-being is still quite scarce, thereby underscoring the needs and significance of a theoretically based study in this regard. This study tested the credibility and validity of the proposed model in the context of the recent COVID-19 pandemic lockdown in China, which is one of the first in recent times.

With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non-native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross-language information retrieval (CLIR) systems through the means of personalized query expansion.

The user profiles consist of weighted terms computed by using frequency-based methods such as tf-idf and BM25, as well as various latent semantic models trained on monolingual documents and cross-lingual comparable documents. This paper also proposes an automatic evaluation method for comparing various user profile generation techniques and query expansion methods.

Experimental results suggest that latent semantic-weighted user profile representation techniques are superior to frequency-based methods, and are particularly suitable for users with a sufficient amount of historical data. The study also confirmed that user profiles represented by latent semantic models trained on a cross-lingual level gained better performance than the models trained on a monolingual level.

Previous studies on personalized information retrieval systems have primarily investigated user profiles and personalization strategies on a monolingual level. The effect of utilizing such monolingual profiles for personalized CLIR remains unclear. The current study fills the gap by a comprehensive study of user profile representation for personalized CLIR and a novel personalized CLIR evaluation methodology to ensure repeatable and controlled experiments can be conducted.

The deep learning-based recommender framework (DLRF) is based on an improved long short-term memory (LSTM) structure with additional controllers; thus, it considers contextual information for state transition. It also handles irregularities in the data to enhance performance in generating recommendations while modelling short-term preferences. An algorithm named a multi-preference integrated algorithm (MPIA) is proposed to have dynamic integration of both kinds of user preferences aforementioned. Extensive experiments are made using Amazon benchmark datasets, and the results are compared with many existing recommender systems (RSs).

RSs produce quality information filtering to the users based on their preferences. In the contemporary era, online RSs-based collaborative filtering (CF) techniques are widely used to model long-term preferences of users. With deep learning models, such as recurrent neural networks (RNNs), it became viable to model short-term preferences of users. In the existing RSs, there is a lack of dynamic integration of both long- and short-term preferences. In this paper, the authors proposed a DLRF for improving the state of the art in modelling short-term preferences and generating recommendations as well.

The results of the empirical study revealed that the MPIA outperforms existing algorithms in terms of performance measured using metrics such as area under the curve (AUC) and F1-score. The percentage of improvement in terms AUC is observed as 1.3, 2.8, 3 and 1.9% and in terms of F-1 score 0.98, 2.91, 2 and 2.01% on the datasets.

The algorithm uses attention-based approaches to integrate the preferences by incorporating contextual information.