Difference between revisions of "COVID-19"
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| − | + | Psychidemic: Measuring the Spatio-Temporal Psychological Impact of Novel Coronavirus with a Social Quality Index | |
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| + | Experts have warned about severe social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). Building upon past successful efforts involving social media big data analysis for epidemiology and public health research, such as drug abuse (leading to an FDA warning), mental health, harassment, and GBV, we undertook the analysis of over 800 million tweets and over 700,000 news articles related to COVID-19 to explore a variety of questions such as: Q1: How can we use social media to measure psychological and social impact in (near) real-time? Q2: How does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? Q3: How do GenZ and Millennials express themselves in the outbreak, particularly in the context of mental health and addiction? This research involves the use of the knowledge-infused natural language processing developed at the AI Institute. It involves infusing (deeply integrating) deep domain knowledge (e.g., mental health-related knowledge from DSM-5 and addition related knowledge captured by the Drug Abuse Ontology) with the deep learning techniques. | ||
Revision as of 15:43, 3 July 2020
Psychidemic: Measuring the Spatio-Temporal Psychological Impact of Novel Coronavirus with a Social Quality Index
Abstract: Experts have warned about severe social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). Building upon past successful efforts involving social media big data analysis for epidemiology and public health research, such as drug abuse (leading to an FDA warning), mental health, harassment, and GBV, we undertook the analysis of over 800 million tweets and over 700,000 news articles related to COVID-19 to explore a variety of questions such as: Q1: How can we use social media to measure psychological and social impact in (near) real-time? Q2: How does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? Q3: How do GenZ and Millennials express themselves in the outbreak, particularly in the context of mental health and addiction? This research involves the use of the knowledge-infused natural language processing developed at the AI Institute. It involves infusing (deeply integrating) deep domain knowledge (e.g., mental health-related knowledge from DSM-5 and addition related knowledge captured by the Drug Abuse Ontology) with the deep learning techniques.
