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This study explored how the use of different social media is related to subjective well-being among university students during the COVID-19 pandemic in Japan.

We surveyed 1,681 university students in the Kanto region of Japan in May 2021 to investigate how social media use relates to subjective well-being. We also examined the effects of self-consciousness and friendship, self-presentation desire, generalized trust, online communication skills, depression tendency and social support from others.

The responses revealed 15 possible patterns of social media usage on four widely used social media in Japan (LINE, Twitter, Instagram and Facebook). We selected users with the top five patterns for further statistical analyses: LINE/Twitter/Instagram/Facebook, LINE/Twitter/Instagram, LINE/Twitter, LINE/Instagram and LINE only. Overall, self-establishment as a factor of self-consciousness and friendship, and social support from others had positive effects on the improvement of subjective well-being, whereas depression tendency had negative effects on their subjective well-being regardless of their usage patterns, of which the results of social support from others and depression tendency were consistent with the results of previous studies. Regarding other factors, they had different effects on subjective well-being due to different patterns. Effects on subjective well-being from self-indeterminate and self-independency as factors of self-consciousness and friendship, praise acquisition, self-appeal and topic avoidance as factors of self-presentation desire were observed.

This is among the earliest studies on the relationship between young generations’ social media use and subjective well-being through social media usage patterns during the COVID-19 pandemic in Japan.

This study aims to clarify the effects of different patterns of Facebook, Twitter and Instagram usage on user loneliness and well-being in Japan.

Based on responses to a self-report questionnaire in Japan, 155 university students were separated into 4 groups: users of Twitter only, users of Twitter and Facebook, users of Twitter and Instagram and users of all three social media. The effects of social media usage on loneliness and well-being for each group were analysed.

No social media usage effects on loneliness or well-being were detected for those who used only Twitter or both Twitter and Instagram. For those using both Twitter and Facebook, loneliness was reduced when users accessed Twitter and Facebook more frequently but was increased when they posted more tweets. Users of all three social media were lonelier and had lower levels of well-being when they accessed Facebook via PC longer; whereas their their access time of Facebook via smartphones helped them decrease loneliness and improve their levels of well-being.

The findings reported here provide possible explanations for the conflicting results reported in previous research by exploring why users choose different social media platforms to communicate with different groups of friends or acquaintances and different usage patterns that affect their loneliness and well-being.

This study investigated the relationship between generalised trust and psychological well-being in college students, considering the social support obtained from their social networks via Twitter and face-to-face (FTF) interactions. Initially, the authors planned to collect data at the beginning of the first semester in 2019 for fine-tuning the model as a pilot study, and in 2020 for the main study. However, due to the coronavirus disease 2019 (COVID-19) pandemic, the data helped authors to analyse changes in young people's psychological situation before and during the pandemic in Japan.

The study conducted a self-report survey targeting college students in the Kanto region in Japan. Data were collected from mid-May to the end of June 2019, as well as in early to mid-June 2020, with 304 and 584 responses, respectively. The collected data were analysed using structural equation modelling and a multiple regression analysis.

The findings using the 2019 data set indicated that (a) students mostly used Twitter for information gathering and sharing of hobbies, and they received both informatics and emotional support from Twitter, and from FTF interactions; (b) there were direct positive effects of generalised trust and social skills on their psychological well-being; and (c) students with lower levels of generalised trust tended to interact with very intimate individuals using Twitter to obtain social support, which did not have any effects on their improvement of psychological well-being. From the 2020 data set, the authors also found that, like 2019, generalised trust and social skills had direct effects on the improvement of psychological well-being. Additionally, we observed that students spent more time using Twitter and received more emotional support from it, as most people tried not to meet other people in person due to the first State of Emergency in Japan. Similarly, the authors found that in 2019, only social support from very intimate partners via FTF communication had slightly significant effects on improving their psychological well-being, whereas in 2020, their expectation for social networks via FTF had decreased their levels of psychological well-being, but their social support from Twitter had slightly significant effects on their improvement of psychological well-being. One of the main reasons for this might be due to the challenge of meeting with others in person, and therefore, social support from Twitter partially played a role that traditionally was only beneficial through FTF communication.

We understand that this is one of the few social psychological studies on social media that collected data both before and during the COVID-19 pandemic. It provides unique evidence in demonstrating how the COVID-19 pandemic has changed college students communication behaviours.

Existing clothing parsing methods make little use of dataset-level information. This paper aims to propose a novel clothing parsing method which utilizes higher-level outfit combinatorial consistency knowledge from the whole clothing dataset to improve the accuracy of segmenting clothing images.

In this paper, the authors propose an Outfit Memory Net (OMNet) that augments original feature by aggregating dataset-level prior clothing combination information. Specifically, the authors design an Outfit Matrix (OM) to represent clothing combination information of single image and an Outfit Memory Module (OMM) to store the clothing combination information of all images in the training set, i.e. dataset-level clothing combination information. In addition, the authors propose a Multi-scale Aggregation Module (MAM) to aggregate the clothing combination information in a multi-scale manner to solve the problem of large variance in the scale of objects in the clothing images.

Experiments on Colorful Fashion Parsing Dataset (CFPD) dataset show that the authors' method achieves 93.15% pixel accuracy (PA) and 51.24% mean of class-wise intersection over union (mIoU), which are satisfactory parsing results compared with existing methods such as PSPNet, DANet and DeepLabV3. Moreover, through comparing the segmentation accuracy of different methods for each category, MAM could effectively improve the segmentation of small objects.

With the rise of various online shopping platforms and the continuous development of deep learning technology, emerging applications such as clothing recommendation, matching, classification and virtual try-on system have emerged in the clothing field. Clothing parsing is the key technology to realize these applications. Therefore, improving the accuracy of clothing parsing is necessary.