An Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach.

Background: The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these 2 domains has not been explored. To address the ongoing drug epidemic, we analyzed drug-related content on Twitter (subsequently rebranded X), with a specific focus on lyrics.

Investigating COVID-19's Impact on Mental Health: Trend and Thematic Analysis of Reddit Users' Discourse.

Background: The COVID-19 pandemic has resulted in heightened levels of depression, anxiety, and other mental health issues due to sudden changes in daily life, such as economic stress, social isolation, and educational irregularity. Accurately assessing emotional and behavioral changes in response to the pandemic can be challenging, but it is essential to understand the evolving emotions, themes, and discussions surrounding the impact of COVID-19 on mental health.

Objective: This study aims to understand the evolving emotions and themes associated with the impact of COVID-19 on mental health support groups (eg, r/Depression and r/Anxiety) on Reddit (Reddit Inc) during the initial phase and after the peak of the pandemic using natural language processing techniques and statistical methods.

Distinguishing the Effect of Time Spent at Home during COVID-19 Pandemic on the Mental Health of Urban and Suburban College Students Using Cell Phone Geolocation.

Unlabelled: The aim of this study was to assess the correlation of depression and anxiety with time spent at home among students at two universities-one urban and the other suburban-during the COVID-19 pandemic.

Methods: Geolocation data from the smartphones of 124 participants were collected between February 2021 and May 2021. The level of depression was estimated by the PHQ-9 and PHQ-2 screening tools, and anxiety scores were estimated by the GAD-2 and GAD-7 screening tools.

An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere.

As the problem of drug abuse intensifies in the U.S., many studies that primarily utilize social media data, such as postings on Twitter, to study drug abuse-related activities use machine learning as a powerful tool for text classification and filtering.

Preserving differential privacy in convolutional deep belief networks.

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc.

Ontology-based Deep Learning for Human Behavior Prediction with Explanations in Health Social Networks.

Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and the likelihood that the systems actually will be adopted, thus driving engagement and loyalty.

A deep learning approach for human behavior prediction with explanations in health social networks: social restricted Boltzmann machine (SRBM).

Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and the likelihood that the systems will be actually adopted, thus driving engagement and loyalty.

Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network.

Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence.

Topic-Aware Physical Activity Propagation with Temporal Dynamics in a Health Social Network.

Modeling physical activity propagation, such as activity level and intensity, is a key to preventing obesity from cascading through communities, and to helping spread wellness and healthy behavior in a social network. However, there have not been enough scientific and quantitative studies to elucidate how social communication may deliver physical activity interventions. In this work, we introduce a novel model named opic-aware ommunity-level hysical Activity Propagation with emporal Dynamics (TCPT) to analyze physical activity propagation and social influence at different granularities (i.

Topic-Aware Physical Activity Propagation in a Health Social Network.

Modeling physical activity propagation, such as physical exercise level and intensity, is the key to preventing the conduct that can lead to obesity; it can also help spread wellness behavior in a social network.