Analyzing Twitter to Gain Insights to Refine Interventions for Family Caregivers of Persons with Alzheimer’s Disease during COVID-19
Formal Title: Analyzing Topics and Sentiments from Twitter to Gain Insights to Refine Interventions for Family Caregivers of Persons with Alzheimer’s Disease and Related Dementias (ADRD) during COVID-19
Video
Team Information
Team Members
Nanyi Deng, MS Candidate, Applied Analytics, School of Professional Studies, Columbia University
Faculty Advisor: Sunmoo Yoon, Associate Research Scientist, Department of Medicine, General Medicine, Columbia University Vagelos College of Physicians and Surgeons
Peter Broadwell, PhD, Research Developer, Center for Interdisciplinary Digital Research, Stanford University
Nicole J Davis, PhD, Assistant Professor, School of Nursing, Clemson University
Michelle Odlum, EdD, MPH, Assistant Professor, CUIMC, Columbia University
Carmela Alcantara, PhD, Associate Professor, School of Social Work, CU
Mary Mittelman, Dr. PH, Professor, Department of Psychiatry, Grossman Department of Medicine, NYU
Abstract
Alzheimer's disease and related dementias (ADRD) is the sixth leading cause of death in the United States. More than 16 million family members or friends provide care for people with ADRD in their homes every year. Sentiment analysis applying the Ann algorithm helps to detect affective state of users of social media as calculated via the Ann lexicon, proposing a total emotional valence score.2 The purpose of this study was to detect topics and sentiment in the Tweet corpus to understand emotional distress as a foundation for developing Twitter-based interventions for Hispanic and African American dementia caregivers. We randomly extracted Tweets mentioning dementia/Alzheimer’s caregiving related terms (n= 58,094Tweets) from Aug 23, 2019 to Sep, 14, 2020 via an API. We applied natural language processing to identify topics and sentiments from the Tweet corpus and compared emotional valence scores of pre (through 2019) and post COVID-19 (2020-). The mean emotional valence score decreased signicantly from 1.18 (SD 1.57; range -7.1 to 7.9) to 0.86 (SD 1.57; range -5.5 to 6.85) after COVID-19 (difference -0.32 CI: -0.35, -0.29). Interestingly, topics related to caregiver emotional distress (e.g., depression, helpless, stigma, lonely, elder abuse), and caregiver coping (e.g, resilience, love, reading books and poems) increased, and late stage of dementia caregiving (e.g., nursing home placement, hospice, palliative care) decreased in prevalence. Application of topic modeling and sentiment analysis of streaming social media Twitter provides the foundation for research insights regarding Alzheimer’s caregiving mental health needs for family caregivers of a person with ADRD.Contact this Team
Team Contact: Nanyi Deng (use form to send email)
