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Malaysia transitions from a pandemic to an endemic state, beginning on April 1, 2022. The dietary intake and physical activity of university students were altered during the COVID-19 pandemic confinement period, but there is limited information on this topic in the post-COVID-19 period. This cross-sectional study aims to understand gender differences in dietary intake and physical activity among university students and investigate the factors influencing their body mass index (BMI) in the post-COVID-19 period.

The questionnaire used in this study comprised three main segments: sociodemographic information, a semiquantitative food frequency questionnaire and the International Physical Activity Questionnaire.

Results indicated that males exhibited significantly higher (p < 0.05) mean intakes of total dietary energy and macronutrients compared to females. Compared to the Malaysian Recommended Nutrient Intakes 2017, 76.4% of males and 68.8% of females had a sufficient daily energy intake. The majority of males (52.7%) and females (46.4%) were moderately physically active. Regression analysis revealed that male university students with lower fiber intake (ß = −0.309, p = 0.002) were significantly associated with higher BMI reading after confounding adjustment. However, a similar trend was not observed in female university students.

Understanding the gender differences in dietary intake and physical activity can provide insights into the development of effective gender-specific health promotion strategies and the formulation of policies aimed at promoting healthier lifestyles among university students in the post-COVID-19 pandemic era.

This study provides insights into the gender differences in the dietary and physical activity habits of university students, as well as the factors influencing their BMI in the post-COVID-19 pandemic era.

For an enterprise, it is essential to win as many customers as possible. The key to successfully winning customers is often determined by understanding the personality characteristics of the object of communication in order to employ an effective communication strategy. An enterprise needs to obtain the personality information of target or potential customers. However, the traditional method for personality evaluation is extremely costly in terms of time and labor, and it cannot acquire customer personality information without their awareness. Therefore, the manner in which to effectively conduct automated personality predictions for a large number of objects is an important issue. The paper aims to discuss these issues.

The diverse social media that have emerged in recent years represent a digital platform on which users can publicly deliver speeches and interact with others. Thus, social media may be able to serve the needs of automated personality predictions. Based on user data of Facebook, the main social media platform around the world, this research developed a method for predicting personality types based on interaction logs.

Experimental results show that the Naïve Bayes classification algorithm combined with a feature selection algorithm produces the best performance for predicting personality types, with 70-80 percent accuracy.

In this research, the dominance, inducement, submission, and compliance (DISC) theory was used to determine personality types. Some specific limitations were encountered. As Facebook was used as the main data source, it was necessary to obtain related data via Facebook’s API (FB API). However, the data types accessible via FB API are very limited.

This research serves to build a universal model for social media interaction, and can be used to propose an efficient method for designing interaction features.

This research has developed an approach for automatically predicting the personality types of network users based on their Facebook interactions.