BACKGROUND: Social media is acknowledged by regulatory bodies (eg, the Food and Drug Administration) as an important source of patient experience data to learn about patients' unmet needs, priorities, and preferences. However, current methods rely ei...
This scoping review aimed to identify and synthesize the evidence in existing nursing studies that used natural language processing to analyze social media data, and the relevant procedures, techniques, tools, and ethical issues. Social media has w...
In recent years, online customer reviews and social media platforms have significantly impacted individuals' daily lives. Despite the generally short nature of textual content on these platforms, they convey a wide range of user sentiments. However, ...
Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning (DL) models have been increas...
OBJECTIVES: This study investigates whether reducing mental illness stigma, enhancing help-seeking efficacy, and maintaining positive mental health mediate the relationship between the recognition of mental disorders and help-seeking attitudes.
BACKGROUND: The stigmatisation of gamblers, particularly those with a gambling disorder, and self-stigmatisation are considered substantial barriers to seeking help and treatment. To develop effective strategies to reduce the stigma associated with g...
BACKGROUND: The COVID-19 pandemic intensified the challenges associated with mental health and substance use (SU), with societal and economic upheavals leading to heightened stress and increased reliance on drugs as a coping mechanism. Centers for Di...
As social media platforms evolve, hate speech increasingly manifests across multiple modalities, including text, images, audio, and video, challenging traditional detection systems focused on single modalities. Hence, this research proposes a novel M...
BACKGROUND: Depression affects more than 350 million people globally. Traditional diagnostic methods have limitations. Analyzing textual data from social media provides new insights into predicting depression using machine learning. However, there is...
BACKGROUND: The COVID-19 pandemic has had a profound effect on the daily routines of individuals and has influenced various facets of society, including healthcare systems, economy, education, and more. With lockdown and social distancing measures, p...