相关论文: Correlating Twitter Language with Community-Level …

Social media data provides propitious opportunities for public health research. However, studies suggest that disparities may exist in the representation of certain populations (e.g., people of lower socioeconomic status). To quantify and…

Post-Traumatic Stress Disorder (PTSD) is a multifaceted mental health condition, particularly challenging for individuals with pre-existing medical conditions. This review critically examines the intersection of PTSD and chronic illnesses…

Health related social media mining is a valuable apparatus for the early recognition of the diverse antagonistic medicinal conditions. Mostly, the existing methods are based on machine learning with knowledge-based learning. This working…

The COVID-19 pandemic has had adverse effects on both physical and mental health. During this pandemic, numerous studies have focused on gaining insights into health-related perspectives from social media. In this study, our primary…

Breast cancer is a significant public health concern and is the leading cause of cancer-related deaths among women. Despite advances in breast cancer treatments, medication non-adherence remains a major problem. As electronic health records…

Predictive models over social media language have shown promise in capturing community outcomes, but approaches thus far largely neglect the socio-demographic context (e.g. age, education rates, race) of the community from which the…

Personal values have significant influence on individuals' behaviors, preferences, and decision making. It is therefore not a surprise that personal values of a person could influence his or her social media content and activities. Instead…

Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via…

We present a study of the relationship between gender, linguistic style, and social networks, using a novel corpus of 14,000 Twitter users. Prior quantitative work on gender often treats this social variable as a female/male binary; we…

Social networks are rich source of data to analyze user habits in all aspects of life. User's behavior is decisive component of a health system in various countries. Promoting good behavior can improve the public health significantly. In…

Social media may disseminate medical claims that highlight misleading correlations between social identifiers and diseases due to not accounting for structural determinants of health. Our research aims to identify biased medical claims on…

Our paper investigates the use of discourse embedding techniques to develop a community recommendation system that focuses on mental health support groups on social media. Social media platforms provide a means for users to anonymously…

Data in the healthcare domain arise from a variety of sources and modalities, such as x-ray images, continuous measurements, and clinical notes. Medical practitioners integrate these diverse data types daily to make informed and accurate…

Understanding the dynamics of social interactions is crucial to comprehend human behavior. The emergence of online social media has enabled access to data regarding people relationships at a large scale. Twitter, specifically, is an…

This paper presents our approaches for the SMM4H24 Shared Task 5 on the binary classification of English tweets reporting children's medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second…

This paper explores the use of language models to predict 20 human traits from users' Facebook status updates. The data was collected by the myPersonality project, and includes user statuses along with their personality, gender, political…

Language use offers valuable insight into affective disorders such as bipolar disorder (BD), yet past research has been cross-sectional and limited in scale. Here, we demonstrate that social media records can be leveraged to study…

Trustfulness -- one's general tendency to have confidence in unknown people or situations -- predicts many important real-world outcomes such as mental health and likelihood to cooperate with others such as clinicians. While data-driven…

We propose a pipeline for gaining insights into complex diseases by training LLMs on challenging social media text data classification tasks, obtaining explanations for the classification outputs, and performing qualitative and quantitative…

Public opinion reflects and shapes societal behavior, but the traditional survey-based tools to measure it are limited. We introduce a novel approach to probe media diet models -- language models adapted to online news, TV broadcast, or…