Using Twitter to Enhance the Social Support of Hispanic and Black Dementia Caregivers
Phase: NA
Conditions: Loneliness, Emotional Stress
Sponsor: Columbia University

Using Twitter to Enhance the Social 
Support of Hispanic and Black 
Dementia Caregivers
NCT03865498
IRB approved on 09/13/2021

Study Description 
1.
Study Purpose and Rationale
The purpose of this study is to evaluate the impact of the Twitter-based intervention on Tweet content and
social network structures of Hispanic and Black dementia caregiver networks. The prevalence of dementia is
higher in Hispanics and Blacks than non-Hispanic Whites.1, 2 Moreover, the caregiving demands in dementia
exceed that of other chronic conditions.3, 4 Consequently, dementia caregivers often experience social isolation
and loneliness and decreased health status, and lower quality of life.5-8 Social support interventions have been
demonstrated to help mitigate these issues.5, 9 The expansion of social media use, particularly Twitter, among
Hispanics and Blacks,10-15 offers great promise for improving social support for Hispanic and Black dementia
caregivers.16 In addition, the availability of large volumes of Tweets and associated metadata make it possible
to apply innovative data science methods to characterize engagement in a social support intervention and to
assess its efficacy over time.17-27 However, analytic frameworks for analyzing content and social network
structures in Twitter communities are relatively new and evolving rapidly, and there has been limited use of
Twitter for intervention delivery.16
Our research team has conducted federally-funded research with Twitter data since 2009 and published a 
practical tutorial on Twitter mining in the American Journal of Preventive Medicine in 2013.25 More recently, 
through the New York City Hispanic Dementia Caregiver Research Program (R01 NR014430) and a 
supplement (R01 NR014430-03S2) which focused on understanding mechanisms of social support in Twitter, 
we have developed a Tweet corpus of >2.6 million English and Spanish Tweets related to dementia caregiving. 
Moreover, we have applied data science-based cultural analytic techniques for Twitter data mining and 
developed innovation visualizations for Twitter data.17, 19-27 Analyses of Tweet topics detected a high frequency 
of social support topics and revealed that the sentiments of Hispanic dementia caregivers were primarily 
negative.17, 19-21, 24 Network analyses revealed a high proportion of isolates (i.e., Tweets without responses) and 
a low proportion of balanced micro-networks, suggesting sub-optimal social support in the networks.17-22, 24 We 
also created a database of >1600 online resources for dementia caregiving.17-22, 24 This substantial preliminary 
work provides evidence that our research team is uniquely qualified to operationalize, implement, and evaluate 
a Twitter-based social support intervention for Hispanic and Black dementia caregivers.17-31
2.
Study Design and Statistical Procedures
Design: The study purpose will be achieved through a quasi-experimental design (non-equivalent 2 group pre
and post-test design). The intervention group is anonymous followers of two Twitter accounts established:
the African American/Black dementia caregiver group and the Hispanic dementia caregiver group. No
personally identifiable information (PII) under the definition used by the National Institute of Standards and
Technology (NIST) such as names, addresses, date of birth, and email addresses will be collected. Instead,
we will analyze publicly available Tweet data for network engagement (count of pressing 'like,' 'retweet,' 'reply'
button), emotional valence (auto-calculation from Tweet text messages: "this is so helpful"-emotion score +4, "I
face many challenges"- emotion score -6), topic changes, the volume of isolates, size and number of
communities, and communication types for our outcome variables. To examine general confounders, three
demographic elements (self-reported age, gender, race/ethnicity, un-linkable to name, phone, email, or
address).
The intervention is summarized in table 1 and comprises the posting of one Tweet per day, replying or directly 
messaging followers, liking someone else's Tweet, retweeting someone else's Tweet, and a monthly Twitter 
chat at an established time. The Tweet messages which the IRB will approve from our Aim 2 will be used as 
an intervention. In addition, Twitter who re-confirm Twitter terms of the agreement, including privacy policy 
and terms of service (Appendix attached), will be required for this study. If someone decides to follow one of 
our accounts (i.e., participate in the intervention) and subsequently changes their mind, they can easily 
change stop following our networks. We will retain our de-identified Twitter followers for our Twitter 
communities in multiple ways: 1) we will invite caregivers in the dementia caregiver registries (including the 
NHiRP project), who agreed to be contacted for the future study, developed by Drs. Luchsinger and Mittelman 
to follow the relevant Twitter accounts; 2) we will use online advertising function for marketing our Twitter 
communities using TwitterAds (15 days in Year 3 and 15 days in Year 4); 3) research flyers will be posted in 
relevant clinics; 4) we will send an invitation via Twitter to the publicly available user IDs (estimated Black user 

IDs-2500, estimated Hispanic user IDs-1800); 4) using the @mention function, we will ask the existing 
organization and community leaders (e.g., @alzassociation, which has 107K followers) to retweet our invitation 
messages to reach out their followers; and 5) monthly, we will determine if there are any new key 
dementia/Alzheimer's Twitter community leaders and send out an invitation to the publicly available user I.D.s. 
Table 1. Twitter-based Intervention 
Concepts 
Tweets 
Time 
Source 
Function 
Recipient 
Instrumental support 
Prepackaged 
Daily** 
library 
Post* 
All followers 
Appraisal support 
Prepackaged 
Daily** 
library 
Post* 
All followers 
Informational support 
Prepackaged 
Daily** 
library 
Post* 
All followers 
Emotional support 
Prepackaged 
Daily** 
library 
Post* 
All followers 
Engage in social network 
Participate in discussion 
Daily 
Tweeters 
Reply, 
Direct message 
All followers 
(Individual) 
Engage in social network 
Answer questions from a 
direct message 
Daily 
Tweeters 
Direct message 
An individual 
Engage in social network 
Answer question from a 
posted message 
Daily 
Tweeters 
Reply 
All followers 
Engage in social network 
Like followers' Tweets 
Daily 
Tweeters 
Like 
All followers 
Engage in social network 
Retweet relevant Tweets 
Daily 
Tweeters 
Retweet 
All followers 
Social support 
Host 2hr Twitter chat 
Monthly 
experts 
#hashtag 
All followers 
*posting: open to all followers. **a message daily (e.g., Monday: Instrumental, Tuesday: Emotional, Wednesday: Informational, Thursday: Appraisal,
Friday: Instrumental, repeat the sequence to avoid weekday and weekend effect.
The setting, Sample, and Power Analysis: The setting is the U.S. The Sample will be the de-identified flowers 
of one of the two dementia caregiver networks (Hispanic, Black). Given our recruitment strategies and 
preliminary work, we anticipate recruiting > 400 de-identified followers in each group. Based on similar Twitter 
communities (e.g., @ dementiatoday), population N is estimated at 153,300 for 365 days.
For statistical analyses, the necessary sample sizes for hypothesis 1 and hypothesis 2 based upon the power 
analysis34 are described below. Hypothesis 1 focuses on within-group differences and Hypothesis 2 on 
between-group differences. The hypotheses include both continuous (emotion valence) and binary (% of 
isolates) outcome variables.  
 
Hypothesis 1: Assuming a correlation of pre- and post-test conservatively: reliability of 300 followers 
will be required. If the correlation between pre- and post is larger, smaller sample sizes are required.  Thus, a 
sample of 300 Blacks and 300 Hispanics is adequate to examine the within-subject change using a paired t-
test. The pre-post-test for binary outcomes (% of isolates) is McNemar's test. The minimum required sample 
size is 208. Thus, the proposed sample sizes will be adequate to test within-group differences.
 
Hypothesis 2: The total minimum required sample size in each ethnic group (Black and Hispanic) will 
be 192 for a total of 385 with effect size 0.332 (Cohen's d). Assuming that α=0.05, 1-β=0.80 (power), the total 
minimum required sample size for the two groups (Black and Hispanic) with effect size 0.1525 (P0-P1) is 318 
(159 per group). 

Data analysis: Extensive processing is required before the actual data analysis, which comprises data 
science methods and statistical tests for our hypotheses. 
Data processing: We will process Twitter data for data analyses through several steps:1) merging of 
data files in our institutional high performance computing (HPC) environment (Habanero), 2) converting XML to 
txt file format into a human-readable format for integrity checking, 3) feature selection, and 4) indexing.35 
Data science analyses for publicly available de-identified data: We will use a variety of software 
packages (R, Python, Nvivo, ORA, NodeXL, SAS) for the data science analyses. Since Twitter user 
demographics and personal data are not directly available, we will apply algorithms to users' Tweets and 
images to estimate their demographics, including those considered Social Determinants of Health.36-39 We will 
stratify those variables into categories for data analysis procedures as relevant. To answer research question 
1, we will descriptively characterize (e.g., #Tweets, # followers, #likes, #retweets) Twitter activity in the two 
networks. To address the second research question, we will apply Syuzhet sentiment analysis and LDA to 
characterize negative affect scores and topic models, respectively. For the third research question, we will 
compute the network measures listed in Table 1. 
 
Emotional valence, expressing topics, and conversational styles in macro-,meso- and micro-level among 
de-identified Twitter followers will be computed from publicly available Tweet data. Unlike the traditional 
methods, data science methods such as natural language processing (NLP) and network analyses enable us 
to detect emotional valence score, changes in topics, volume of isolates, and communication styles without 
asking the users any questionnaires. The following table summarizes concepts, measurements, units of 
analysis, and data science analytic methods.  
Statistical Analyses: Pre-post comparisons will be examined from baseline (3 months pre-intervention) 
to 3 months (9-12) months after initiation of the intervention. Analyses of pre-post mean differences will be 
performed using SAS PROC TTEST, PROC Mixed, PROC GLIMMIX, and PROC CATMOD, with adjustments for the 
correlation within participants induced by repeated measures and for unreliability and missing data treatment. 
The continuous outcome will be analyzed using SAS PROC TTEST, PROC Mixed, and the binary outcome will 
Table 1. Concepts, Measures, Data Science Methods and Units of Analysis 
Concept 
Measures§ 
Data Science 
Methods  
Unit 
Content 
Emotional valence40 
Emotional valence score 18, 40-45 
detected from text data  
(e.g., "This is so helpful" – emotion 
score +4, "I am sad"- emotion score -6) 
Syuzhet packages 46 
*** 
Social support 
(informational, 
instrumental, 
emotional, appraisal) 
47-50
Topic changes in social support 17, 19-21 
Topic modeling using  
hierarchical 51  and n-
gram embedded LDA 
algorithms 51-54 
*** 
Structure 
Social network: Macro-
level 55 
The volume of isolates, Network type,  
Density, Centralization, Cohesiveness 
26, 27, 55, 56
Network analysis  17-
20, 23, 24, 26, 27
*** 
Social network: Meso-
level 57, 58
Size and number of communities, the 
volume of the community activities, 
leaders of community 26, 27, 56, 57 
Clauset-Newman-
Moore 59 
Girvan-Newman58 
** 
Social network: Micro-
level 60 
16 communication types among triad, 
number of individual posting activities 
26, 27, 56, 57, 60
Triad census 
analysis 60 
* 
* individual level, ** community level, *** global network level, §all measures in real-time) 

be analyzed using PROC GLIMMIX or PROC CATMOD (for the McNemar test), depending on the missing data. 
We will compare measures among the two groups using independent t-tests for continuous variables and Chi-
square tests for proportions to test the second hypothesis. Machine learning-based trend analysis 61, 62 will be 
applied for the continuously detected emotional valence score.  
3. Study Procedures
Procedure: Publicly available Tweet data (e.g., Tweets, time stamp, self-reported location) from de-identified 
Twitter followers of our Twitter sites will be obtained from the public database. No personally identifiable 
information (PII) under the definition used by the National Institute of Standards and Technology (NIST) such 
as names, addresses, date of birth, and email addresses will be collected. We will analyze publicly available 
Tweet data for emotional valence (auto-estimation from Tweet messages), topic changes, the volume of 
isolates, size and number of communities, and communication types for our outcome variables. In addition, 
three demographic elements (i.e., self-reported age, gender, race/ethnicity, which are un-linkable to their PIIs) 
will be collected to examine general confounders. Again, no PII will be used or accessed. 
Intervention: Two Tweeters, including a bilingual Hispanic, will strictly follow the intervention delivery protocol 
developed in Aim 2 with guidance from the Tweet-S2 Expert Panel. Daily for 365 days that cross Years 3 and 
4, the Tweeters will post a Tweet from the Tweet library to each Twitter community alternating among those 
related to information, instrumental, emotional, and appraisal support. The Tweeter will also engage in social 
network activities by replying to, liking, and retweeting messages posted by others. This intervention will be 
consistently conducted (e.g., time, center brand themes, designs) to enhance intervention fidelity.63 After a 
Tweet is posted, followers can respond using Twitter functions (e.g., retweet to quote and disseminate our 
Tweet message, posting a Tweet message to express their after thoughts and questions using @mention, like, 
and reply function). In addition, the P.I. will examine the archive files to monitor intervention fidelity and 
reinforce expected behaviors of the Tweeters as needed. 
Publicly available Twitter data collection: During the baseline (3 months pre-intervention) and intervention (365 
days crossing Years 3 and 4) periods, we will collect publicly available Tweet messages and associated 
metadata (e.g., userID, relationship, time stamp, Tweet, Hashtags, Twitter page, geo code, location, type) 
daily. We will also double-check the data integrity (e.g., incompletely captured data) daily. In terms of missing 
Twitter data,64 we will make an effort to minimize missing data in 3 ways: our designated Tweeters and the P.I. 
will be available during data collection, two collection tools (NodeXL and NCapture) will be used, and collection 
of Tweets will be monitored daily to ensure that capture was adequate and with no PII. Figure 1 shows an 
example of publicly available data collected.  
Figure 1. An example of publicly available data collected via NodeXL: Vertex 1 and 2 will be used for macro, 
meso, and micro level network analyses. Tweet text with no personal identifiers will be used for text analyses, 
including emotion score calculation and topic modeling. 

4. Study Drugs or Devices
Not applicable
5. Study Instruments (e.g., Questionnaires, Interview Outlines, Focus Group Guides)
No direct instrumentations such as questionnaires (i.e., survey, interview) will be applied for the outcome
measurers. Instead, emotional valence, topics, and conversational styles in macro-,meso- and micro-level
among anonymous Twitter followers will be computed from the publicly available Tweet data. Unlike the
traditional methods, data science methods such as natural language processing (NLP) and network analyses
enable us to detect emotional valence score, changes in topics, volume of isolates, and communication styles
without asking the users. Table 1 above in Data Analyses summarizes concepts, measurements, units of
analysis, and data science analytic methods.
6. Study Subjects
Inclusion criteria: The anonymous dementia caregivers must be 18 years of age or older, Black or Hispanic, 
living in the U.S. including the U.S. territories, a dementia caregiver with any duration, able to speak English or 
Spanish/bilingual. The participants must agree to terms of conditions of use and privacy policy and rules of one 
of the two dementia caregiver networks (Hispanic and Black), the Twitter user agreement of 
the terms of service, Twitter privacy policy, and Twitter rules, including intellectual property, violence, 
misconduct, abusive behavior, private information and spam and security, and use a smartphone or a feature 
phone (i.e., a cell phone with text messaging). 
Screening/Determining Eligibility 
Upon a Twitter user follows one of our Twitter networks after seeing our advertisement on Twitter or our flyers 
from our clinics and emails, our team will automatically receive a new follower notification from Twitter. Within 
24 hours during the weekdays (72 hours during the weekend), our Tweeter/coordinator will send a screening 
message via the direct message function in Twitter (script attached) and respond if they are eligible to 
participate in our research Twitter networks (script attached). 
When a Twitter user meets our inclusion criteria, the study staff will remind the user regarding our Twitter 
networks as a research study, voluntary participation, and unfollowing our Twitter networking sites at any time. 
Furthermore, using the direct message function via Twitter, our study staffs will ensure that the eligible user 1) 
creates an anonymous Twitter handle if the person uses his/her full name as a Twitter handle, and 2) hides 
their names from the profile setting. When a Twitter user does not meet our inclusion criteria, our study staff 
will request them unfollowing our Twitter network via a direct message function (script attached). Ineligible 
followers will be blocked within 72 hours after the unfollowing request.  
7. Recruitment
Considering 60% attrition rate of anonymous online users, we anticipate to enroll >400 per group, to accrue
up to a total of 800 (400 per group, total minimum required Sample are 318 users: 159 per group) and retain 
anonymous followers (no actual name or phone number/email address on the profile or Twitter handle) for our 
Twitter communities in multiple ways: 1) A flyer will be distributed up to 5 times (per a week) via an email to 
the caregivers, who agreed to be contacted for the future studies, in the dementia caregiver registries; 2) we 
will use an online advertising function for marketing our Twitter communities using TwitterAds (15 days in 
Year 3 and 15 days in Year 4); 3) research flyers will be posted in relevant clinics (i.e., AIM clinic and 
neurology units at CUIMC); 4) using the @mention function, we will ask the community leaders (e.g., 
@alzassociation, which has 107K followers)  to post our flyer; and 5) every month, we will contact relevant 
community leaders within Twitter to post our flyer. This study does not offer any incentives (e.g., no online 
credits, no monetary compensation).
8. Informed Consent with waiver of documentation (45 CFR 46.116)
Informed consent will be obtained with a waiver of documentation (e.g., signature). Anonymous Twitter users 
without PII (no name, email address, phone number, or addresses are linkable) will participate in this study, 
analyzing publicly available de-identified data. Accidental PII exposure will be strictly prohibited (see four 
strategies in privacy protection) and monitored daily. Therefore, this research to post social 

support messages to our Twitter network followers involves minimal risk to the subject. The signature on the 
informed consent document would be the only record linking the subject to the research.  
9. Confidentiality of Study Data
Our study data will comprise 1) publicly available Twitter data and 2) three demographic elements, including 
self-reported age, gender, and race/ethnicity (un-linkable to name, email address, phone number).  For both, 
our team strictly follows the institutional policy for computers and endpoint devices to store the Tweet corpora. 
Data preprocessing and manipulation will occur using the secure data repositories at the Columbia University 
Medical Center, such that no data we collect will be stored out of our control. We will use a secured network for 
the Department of General Medicine for temporary storage for daily data collection, as well as a university-
provided fully encrypted laptop and a university-provided fully encrypted external drive for the long term backup 
purpose. Although our Twitter data does not contain personally identifiable information, all data will be 
managed and stored under our institutional policy which all endpoint devices of our research team should be 
protected with 256-bit encryption (at minimum), which severely restricts access to the data in a far superior 
manner than password protection. For data sharing and preparation, we will use a passcode-protected 
Columbia University Habanero HPC environment for the transient file storage while merging, converting, and 
computing the large volume of data
10.
Privacy Protections
The anonymous followers of our Twitter sites will be informed that no identifiers such as names or email 
addresses will be stored in the data collection. Their PII, such as email addresses and names if the participants 
contacted the study team via email for the recruitment purpose, will be removed from the recruitment 
documentation on the day of data collection. All data will be de-identified before analysis, and no identifying 
information about the individual will be used in reporting the study results.We will implement the following 
protections against risks:
1) Warning: our two dementia caregiver Twitter networks will have a privacy protection reminder with a 
warning sign on their title pages. Our Twitter network sites will include short and relevant privacy 
statements (i.e., sharing aggregate data of age group and estimated gender, how to contact our 
organization's data protection officer.)
2) Reminding: a designated research team member will post a welcome video reminding the target 
audience about the protection of privacy using a Tweet direct message function upon a participant 
following (subscribing) our Twitter sites and post a reminder Tweet about privacy protection once a 
week (i.e., Posting self-identifiable photos, names, email address or phone number will be strictly 
prohibited).
3) Monitoring - Privacy: a designated research team member will monitor user-generated content daily for 
the risks of exposure to PII.
4) Monitoring – Content: A designated research team member will remove Misleading or inappropriate 
content as necessary daily.
11.
Potential Risks
There are some minimal risks specific to this study. First, though we will not collect any direct PIIs nor 
personally identifying photographic images, and all analyses are done at an aggregate level, followers (i.e., site 
users/study participants) may accidentally post texts or images that contain PII. Second, there is a possibility 
that followers may post information that is inaccurate and potentially harmful.
12.
Potential benefits of the proposed research to the subjects and others
Those who participate in the Twitter-based intervention study may perceived increased social support 
(informational, instrumental, emotional, appraisal) and may feel belongingness which is a human basic need. 
13. Importance of the knowledge to be gained
The important contributions of this study to knowledge development are both substantive and methodological. 
The study will extend what is known about dementia caregiving in minority populations and the mechanisms of 
social support. Moreover, the study will expand what is known about using Twitter as a mode for intervention 
delivery and methods for creating culture-specific lexica and culturally-sensitive Tweet messages and analysis 
of Twitter data.  

14. Alternatives
This research involves an intervention that presents no greater than minimal risk to participants. If participants 
change their minds, they can easily change a setting to stop following our Twitter networks. 
15. Data and Safety Monitoring
Although our Tweet corpora only include publicly available Twitter data, our team strictly follows the 
institutional policy for computers and endpoint devices to store the Tweet corpora. Data preprocessing and 
manipulation will occur using the secure data repositories at the Columbia University Irving Medical Center. No 
data we collect will be stored out of our control. We will use a secured network for the General Medicine for 
temporary storage for daily data collection, as well as a university-provided fully encrypted laptop and a 
university-provided fully encrypted external drive for the long term backup purpose. Although our Twitter data 
does not contain personally identifiable information, all data will be managed and stored under our institutional 
policy which all endpoint devices of our research team should be protected with 256-bit encryption (at 
minimum), which severely restricts access to the data in a far superior manner than password protection. For 
data sharing and preparation, we will use a passcode-protected Columbia University Terremoto HPC 
environment for the transient file storage while merging, converting, and computing the large volume of data, 
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