Browsing by Author "Jia X"

Now showing 1 - 6 of 6
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    A cross-sectional online survey of depression symptoms among New Zealand’s Asian community in the first 10 months of the COVID-19 pandemic
    (Taylor and Francis Group, 2023-09-03) Siegert RJ; Zhu A; Jia X; Ran GJ; French N; Johnston D; Lu J; Liu LS
    The COVID-19 pandemic has elevated levels of distress and resulted in anti-Asian discrimination in many countries. We aimed to determine the 10-month prevalence of depression symptoms in Asian adults in New Zealand during the pandemic and to see if this was related to experience of racism. An online survey was conducted and a stratified sample of 402 respondents completed the brief Centre for Epidemiological Studies-Depression (CES-D) scale. Analyses included: descriptive statistics, depression scores by age/gender, factor analysis of the 10 item CES-D and partial correlation network analysis of CES-D items together with questions about experience of racism. Results show that half of the sample reported clinically significant symptoms of depression. Depression was higher among younger participants but there was no gender difference. Internal consistency was high (α = 0.85) for the CES-D which revealed a clear two-factor structure. Network analysis suggested that sleeping problems might be the bridge between experiences of racism and depression. The prevalence of low mood was high with clinically significant levels of depressive symptoms. Depression was higher in younger people and had a modest positive correlation with personal experience of racism.
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    Anisotropic span embeddings and the negative impact of higher-order inference for coreference resolution: An empirical analysis
    (Cambridge University Press, 2024-01-25) Hou F; Wang R; Ng S-K; Zhu F; Witbrock M; Cahan SF; Chen L; Jia X
    Coreference resolution is the task of identifying and clustering mentions that refer to the same entity in a document. Based on state-of-the-art deep learning approaches, end-to-end coreference resolution considers all spans as candidate mentions and tackles mention detection and coreference resolution simultaneously. Recently, researchers have attempted to incorporate document-level context using higher-order inference (HOI) to improve end-to-end coreference resolution. However, HOI methods have been shown to have marginal or even negative impact on coreference resolution. In this paper, we reveal the reasons for the negative impact of HOI coreference resolution. Contextualized representations (e.g., those produced by BERT) for building span embeddings have been shown to be highly anisotropic. We show that HOI actually increases and thus worsens the anisotropy of span embeddings and makes it difficult to distinguish between related but distinct entities (e.g., pilots and flight attendants). Instead of using HOI, we propose two methods, Less-Anisotropic Internal Representations (LAIR) and Data Augmentation with Document Synthesis and Mention Swap (DSMS), to learn less-anisotropic span embeddings for coreference resolution. LAIR uses a linear aggregation of the first layer and the topmost layer of contextualized embeddings. DSMS generates more diversified examples of related but distinct entities by synthesizing documents and by mention swapping. Our experiments show that less-anisotropic span embeddings improve the performance significantly (+2.8 F1 gain on the OntoNotes benchmark) reaching new state-of-the-art performance on the GAP dataset.
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    Learning and integration of adaptive hybrid graph structures for multivariate time series forecasting
    (Elsevier Inc., 2023-11-01) Guo T; Hou F; Pang Y; Jia X; Wang Z; Wang R
    Recent status-of-the-art methods for multivariate time series forecasting can be categorized into graph-based approach and global-local approach. The former approach uses graphs to represent the dependencies among variables and apply graph neural networks to the forecasting problem. The latter approach decomposes the matrix of multivariate time series into global components and local components to capture the shared information across variables. However, both approaches cannot capture the propagation delay of the dependencies among individual variables of a multivariate time series, for example, the congestion at intersection A has delayed effects on the neighboring intersection B. In addition, graph-based forecasting methods cannot capture the shared global tendency across the variables of a multivariate time series; and global-local forecasting methods cannot reflect the nonlinear inter-dependencies among variables of a multivariate time series. In this paper, we propose to combine the advantages of both approaches by integrating Adaptive Global-Local Graph Structure Learning with Gated Recurrent Units (AGLG-GRU). We learn a global graph to represent the shared information across variables. And we learn dynamic local graphs to capture the local randomness and nonlinear dependencies among variables. We apply diffusion convolution and graph convolution operations to global and dynamic local graphs to integrate the information of graphs and update gated recurrent unit for multivariate time series forecasting. The experimental results on seven representative real-world datasets demonstrate that our approach outperforms various existing methods.
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    Online Health Information Seeking Behavior: A Systematic Review
    (MDPI (Basel, Switzerland), 2021-12) Jia X; Pang Y; Liu LS
    The last five years have seen a leap in the development of information technology and social media. Seeking health information online has become popular. It has been widely accepted that online health information seeking behavior has a positive impact on health information consumers. Due to its importance, online health information seeking behavior has been investigated from different aspects. However, there is lacking a systematic review that can integrate the findings of the most recent research work in online health information seeking, and provide guidance to governments, health organizations, and social media platforms on how to support and promote this seeking behavior, and improve the services of online health information access and provision. We therefore conduct this systematic review. The Google Scholar database was searched for existing research on online health information seeking behavior between 2016 and 2021 to obtain the most recent findings. Within the 97 papers searched, 20 met our inclusion criteria. Through a systematic review, this paper identifies general behavioral patterns, and influencing factors such as age, gender, income, employment status, literacy (or education) level, country of origin and places of residence, and caregiving role. Facilitators (i.e., the existence of online communities, the privacy feature, real-time interaction, and archived health information format), and barriers (i.e., low health literacy, limited accessibility and information retrieval skills, low reliable, deficient and elusive health information, platform censorship, and lack of misinformation checks) to online health information seeking behavior are also discovered.
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    Stigmatising and Racialising COVID-19: Asian People’s Experience in New Zealand
    (Springer Nature, 2022-11-11) Liu LS; Jia X; Zhu A; Ran GJ; Siegert R; French N; Johnston D
    The Asian community — the second largest non-European ethnic community in New Zealand — plays an important role in combatting the COVID-19 pandemic, evidenced by their active advocation for border control and mass masking. Despite the long history of racial discrimination against the Asian population, the Asian community has experienced certain degrees of racial discrimination associated with the stigmatisation as the cause of the COVID-19 outbreak in New Zealand. Based on data from a quantitative online survey with 402 valid responses within the Asian communities across New Zealand and the in-depth interviews with 19 Asian people in Auckland, New Zealand, this paper will illustrate Asian people’s experience of racial discrimination and stigmatisation during the pandemic in the country. The survey shows that since the outbreak of COVID-19, under a quarter of the participants reported experiencing discrimination, and a third reported knowing an immediate contact who had experienced discrimination. However, when looking beyond their immediate social circle, an even higher proportion reported noticing racism and stigmatisation through the traditional or social media due to COVID-19. Major variations of the degree of racial discrimination experienced are determined by three demographic variables: ethnicity, age, and region. The in-depth interviews largely echoed the survey findings and highlighted a strong correlation between the perceived racial discrimination among the local Asian community and the stigmatisation associated with COVID-19. These findings are important for improving the way we manage future pandemics and other disasters within the context of the UN Sendai Framework for Disaster Risk Reduction.
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    Understanding consumers' continuance intention to watch streams: A value-based continuance intention model
    (Frontiers Media S.A., 2023-03-01) Jia X; Pang Y; Huang B; Hou F; Xie T
    INTRODUCTION: Live stream-watching has become increasingly popular worldwide. Consumers are found to watch streams in a continuous manner. Despite its popularity, there has been limited research investigating why consumers continue to watch streams. Previously, the expectation-confirmation theory (ECT) has been widely adopted to explain users' continuance intention. However, most current ECT-based models are theoretically incomplete, since they only consider the importance of perceived benefits without considering users' costs and sacrifices. In this paper, we propose a value-based continuance intention model (called V-ECM), and use it to investigate factors influencing consumers' continuance intention to watch streams. METHODS: Our hypotheses were tested using an online survey of 1,220 consumers with continuance stream-watching experiences. RESULTS: Results indicate that perceived value, a process of an overall assessment between users' perceived benefits and perceived sacrifices, is proved to be a better variable than perceived benefits in determining consumers' continuance watching intention. Also, compared with other ECT-based models, V-ECM is a more comprehensive model to explain and predict consumers' continuance intention. DISCUSSION: V-ECM theoretically extends ECT-based studies, and it has potential to explain and predict other continuance intentions in online or technology-related contexts. In addition, this paper also discusses practical implications for live streaming platforms with regards to their design, functions and marketing.