During the COVID-19 emergency, people must face the invisible threat of uncertain death and comply with social distancing and other related protective measures. But social networking sites (SNSs) like WeChat have emerged as alternative contexts where people can maintain self and obtain gratification, despite the disclosure of self to others often being a challenging issue. To examine the effect of personality factors on people's self-disclosure on SNSs, we develop a model based on the time perspective theory and the socioemotional selectivity theory. We suggest that people's narcissism traits and two types of time perspective (i.e. future and present-hedonistic) have positive effects on their self-disclosure. In addition, we propose that virtual presence and hedonic gratification mediate the effects of both future time perspective and present-hedonistic time perspective. We obtain a sample of 516 WeChat users in China and take ex-ante and post-hoc measures to deal with common method variance. By means of partial least squares-structural equation modelling (PLS-SEM) methods, we validate both our measurement model and the structural model, so we confirm all the hypothesized causal paths in the proposed model. Our study broadens the boundaries of the time perspective theory and the socioemotional selectivity theory, and offers new insights for addressing some of the effects of the COVID-19 pandemic.

Purpose: Nowadays, public opinions during public emergencies involve not only textual contents but also contain images. However, the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis, lacking the combination of multimodal contents. In this paper, we propose to combine texts and images generated in the social media to perform sentiment analysis. Design/methodology/approach: We propose a Deep Multimodal Fusion Model (DMFM), which combines textual and visual sentiment analysis. We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis. BiLSTM is employed to generate encoded textual embeddings. To fully excavate visual information from images, a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy. A multimodal fusion method is implemented to fuse textual and visual embeddings completely, producing predicted labels. Findings: We performed extensive experiments on Weibo and Twitter public emergency datasets, to evaluate the performance of our proposed model. Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models. The introduction of images can boost the performance of sentiment analysis during public emergencies. Research limitations: In the future, we will test our model in a wider dataset. We will also consider a better way to learn the multimodal fusion information. Practical implications: We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies. Originality/value: We consider the images posted by online users during public emergencies on social platforms. The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.