Self-Report IPV on Twitter Intimate partner violence (IPV) is a preventable public health problem that affects millions of people worldwide. Approximately one in four women are estimated to be or have been victims of severe violence at some point in their lives, irrespective of age,... Read More →
Abstract Migraine is a highly prevalent and disabling neurological disorder. However, information about migraine management in real-world settings is limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported... Read More →
Background In recent years, there has been a concerning increase in suicides and self-harm cases, both in the United States and across the world. In the United States, suicide rates have been steadily rising among different age groups and demographics. Globally, suicide rates also remain... Read More →
This study proposes a natural language processing (NLP) architecture to detect breast cancer patients on Twitter based on their self-reports. By utilizing breast cancer related keywords and employing a machine learning classifier, the architecture achieves high accuracy in distinguishing firsthand self-reports of breast cancer, offering... Read More →
This page contains resources associated with multiple studies (completed and ongoing) focusing on COVID-19 Dashboards Research topics/studies: Syndromic surveillance: we are utilizing real-time social media data, natural language processing and machine learning methods to identify and track symptom distributions over time. Localized outbreak detection: we... Read More →
Social media is a source of massive amounts of information. Social media is accessible by large populations and it contains large amounts of health-related chatter posted by users themselves. Therefore, social media often contains health-related information that may not be available from any other sources.... Read More →
