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Inducing Complete Coverage for Immunizations by Channeling Social Media
Last registered on January 02, 2018


Trial Information
General Information
Inducing Complete Coverage for Immunizations by Channeling Social Media
Initial registration date
January 02, 2018
Last updated
January 02, 2018 4:28 PM EST
Primary Investigator
Other Primary Investigator(s)
PI Affiliation
Stanford University
PI Affiliation
World Bank
PI Affiliation
Microsoft Research
PI Affiliation
World Bank
Additional Trial Information
On going
Start date
End date
Secondary IDs
Indonesia’s performance on the health Millennium Development Goals (MDGs) still remains poor. On MDG 4, which explores the mortality rate of children 0-5, Indonesia has made some progress, moving from 97 per 100,000 live births in 1990 to 44 per 100,000 by 2010. However, the rate of progress has declined, and it remains unclear whether the MDG will be met.

One of the key drivers of the progress for child mortality is the rate of complete immunization of children under 5 against a range of childhood diseases, including measles, diphtheria, whooping cough (pertussis), tetanus, tuberculosis, polio and Hepatitis B. Despite their importance, complete immunization coverage against these diseases is quite low in Indonesia – for instance, only 27% of children are fully immunized against polio, and more than 21% of children have not been fully immunized against measles.

The Government of Indonesia has made significant efforts to increase the supply of health services to improve these outcomes. By 2011, only 6% of rural children lacked access to a primary health care center, and almost no urban children lacked access. Only 8% of rural children were born in a village without a midwife, compared with only 2% of urban children. Despite important progress on the supply side, low rates of care-seeking persist and outcomes remain poor.

The Government is seeking to investigate whether electronic media -- social media such as Twitter, as well as conventional text messaging to cell phones -- can help increase vaccinations. In Indonesia, use of Twitter and other social applications – especially those accessible via mobile – has skyrocketed. Indonesia holds the world’s fifth largest number of Twitter accounts, though recent growth has outpaced three of the four nations with larger user bases: Brazil, Japan, and the United Kingdom. On a daily basis, we observe between 400 and 500 million tweets, of which about 75% are original tweets and the remainders are retweets. The Government thus believes that Twitter has an important potential role in spreading social information, such as immunization campaigns.

In addition to the policy interest, there is also a separate set of academic questions about the diffusion of information over networks: how we communicate, on what topics, and whether this induces actions. With the proliferation of new social tools, individuals engage in information exchange in more ways than ever before, and this has implications on political beliefs, take up of social policies, preferences related to care seeking behaviors, among other areas.

This research project will therefore investigate whether disseminating social messages on Twitter and direct text messages (SMS) can have a positive and significant impact on take-up of socially positive behaviors, such as immunization seeking behaviors.
External Link(s)
Registration Citation
Alatas, Vivi et al. 2018. "Inducing Complete Coverage for Immunizations by Channeling Social Media." AEA RCT Registry. January 02. https://doi.org/10.1257/rct.757-1.0.
Former Citation
Alatas, Vivi et al. 2018. "Inducing Complete Coverage for Immunizations by Channeling Social Media." AEA RCT Registry. January 02. http://www.socialscienceregistry.org/trials/757/history/24657.
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Experimental Details
The interventions are social messages related with immunization delivered over Twitter by celebrity accounts.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
(a) propensity to retweet information
(b) knowledge and discussion about immunisation
(c) recent immunization behavior
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
This research project investigates whether disseminating social messages on Twitter can have a positive and significant impact on take-up of socially positive behaviors, such as immunization seeking behaviors. The project involves a Twitter campaign.

Approximately 300 users (including celebrities, immunization-related organizations and non-celebrity Twitter users) are recruited for the Twitter campaign. All of these twitter accounts will publish tweets on immunization, but the tweets will be varied based on (1) whether a celebrity, organization, or ordinary individual is the first to tweet the message, (2) how often the tweet is scheduled to be published, and (3) whether the tweet includes a credibility boost (in the form of mentioning an official immunization-related organization)/content of the tweet.

After the treatments have been implemented, data will be collected from approximately 2000 households via phone surveys, and researchers will also observe the spread of the Twitter campaign within its users.
Experimental Design Details
Randomization Method
Randomization will be done using Matlab
Randomization Unit
The celebrities are randomly split into two phases of campaign. Additionally, we also randomly assign the type of treatments for each tweet sent in the campaign.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Sample size: planned number of observations
approximately 40 celebrities and all their followers (approx. 5 million)
Sample size (or number of clusters) by treatment arms
Approximately 40 celebrities in the total sample, split in two phases of campaign.
Approximate number of tweets in each phase: 450 tweets
Approximate number of tweets by tweet source: 400 direct tweets, 250 joe tweets, 250 org tweets
Approximate number of tweets for each credibility boost type: 300 tweets
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
IRB Approval Date
IRB Approval Number
IRB Name
Stanford University
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)