Applying Participatory Design Mixed-Methods Approaches to Refine Twitter-Based Social Support Interventions for African Americans and Hispanic Family Caregivers of Persons with Dementia.

We applied mixed-methods to refine our first version of the Twitter message library (English 400, translated into Spanish 400) for African Americans and Hispanic family caregivers for a person with dementia. We conducted a series of expert panels to collect quantitative and qualitative data using surveys and in-depth interviews. Using mixed methods to ensure unbiased results, the panelists first independently scored them (1 message/5 panelist) on a scale of 1 to 4 (1: lowest, 4: highest), followed by in-depth interviews and group discussions. Survey results showed that the average score was 3.47, indicating good to excellent (SD 0.35, ranges from 1.8 to 4). Quantitative surveys and qualitative interviews showed different results in emotional support messages.

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 Pandemic.

We randomly extracted Tweets mentioning dementia/Alzheimer's caregiving-related terms (n= 58,094) from Aug 23, 2019, to Sep 14, 2020, via an API. We applied a clustering algorithm and natural language processing (NLP) to publicly available English Tweets to detect topics and sentiment. We compared emotional valence scores of Tweets from before (through the end of 2019) and after the beginning of the COVID-19 pandemic (2020-). Prevalence of topics related to caregiver emotional distress (e.g., depression, helplessness, stigma, loneliness, elder abuse) and caregiver coping (e.g., resilience, love, reading books) increased, and topics related to late-stage dementia caregiving (e.g., nursing home placement, hospice, palliative care) decreased during the pandemic. The mean emotional valence score significantly decreased 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 the advent of COVID-19 (difference -0.32 CI: -0.35, -0.29). The application of topic modeling and sentiment analysis to streaming social media provides a foundation for research insights regarding mental health needs for family caregivers of a person with ADRD during COVID-19 pandemic.

Applying Social Network Analysis to Compare Dementia Caregiving Networks on Twitter in Hispanic and Black Communities.

We applied social network analysis (SNA) on Tweets to compare Hispanic and Black dementia caregiving networks. We randomly extracted Tweets mentioning dementia caregiving and related terms from corpora collected daily via the Twitter API from September 1 to December 31, 2019 (initial corpus: n = 2,742,539 Tweets, random sample n = 549,380 English Tweets, n= 185,684 Spanish Tweets). After removing bot-generated Tweets, we first applied a lexicon-based demographic inference algorithm to automatically identify Tweets likely authored by Black and Hispanic individuals using Python (n = 114,511 English, n = 1,185 Spanish). Then, using ORA, we computed network measures at macro, meso, and micro levels and applied the Louvain clustering algorithm to detect groups within each Hispanic and Black caregiving network. Both networks contained a similar proportion of dyads and triads (Hispanic 88.2%, Black 88.9%), while the Black caregiving network included a slightly larger proportion of isolates (Hispanic 0.8%, Black 4.0%). This study provides useful baseline information on the composition of existing large groups and small groups. In addition, this work provides useful guidance for future recruitment strategies and the design of social support interventions regarding emotional needs for Hispanic and Black dementia caregivers.