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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: Conversations with a concern-addressing chatbot increase COVID-19 vaccination intentions among social media users in Kenya and Nigeria |
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Identification Number: |
doi:10.7910/DVN/YFGDA4 |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2024-12-30 |
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Version: |
1 |
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Bibliographic Citation: |
Rosenzweig, Leah R.; Offer-Westort, Molly, 2024, "Replication Data for: Conversations with a concern-addressing chatbot increase COVID-19 vaccination intentions among social media users in Kenya and Nigeria", https://doi.org/10.7910/DVN/YFGDA4, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication Data for: Conversations with a concern-addressing chatbot increase COVID-19 vaccination intentions among social media users in Kenya and Nigeria |
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Identification Number: |
doi:10.7910/DVN/YFGDA4 |
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Authoring Entity: |
Rosenzweig, Leah R. (University of Chicago) |
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Offer-Westort, Molly (University of Chicago) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Rosenzweig, Leah R. |
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Depositor: |
Rosenzweig, Leah |
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Date of Deposit: |
2024-11-27 |
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Holdings Information: |
https://doi.org/10.7910/DVN/YFGDA4 |
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Study Scope |
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Keywords: |
Social Sciences, Adaptive experiment, digital intervention, political communication, social media, vaccine hesitancy |
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Abstract: |
This study leverages a two-stage response-adaptive experimental design to explore how tailored digital messaging can influence public attitudes toward health interventions, with broader implications for political communication. In public health settings, quickly scaling effective communication is critical; adaptive designs enable efficient learning of relevant treatments. For this study, we recruited 22,052 Facebook users in Kenya and Nigeria to engage in conversations on Messenger about their concerns regarding COVID-19 vaccines. We optimized messaging using an adaptive algorithm, then experimentally evaluated the optimized concern-addressing messaging. We find that the optimized concern-addressing messaging increases COVID-19 vaccine intentions and willingness by 4-5% compared to a control condition, and by 3-4% compared to a public service announcement. We observe the largest treatment effects among those most vaccine hesitant at baseline. Personalized digital messaging interventions offer a scalable communication tool to encourage compliance with public health programs. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Label: |
chatbot_paper_jop.pdf |
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Text: |
paper code log |
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Notes: |
application/pdf |
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Label: |
chatbot_paper_jop.R |
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Text: |
paper code |
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Notes: |
type/x-r-syntax |
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Label: |
chatbot_SI_jop.pdf |
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Text: |
SI code log |
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Notes: |
application/pdf |
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Label: |
chatbot_SI_jop.R |
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Text: |
SI code |
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Notes: |
type/x-r-syntax |
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Label: |
clean_evaluation_data.rds |
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Text: |
evaluation data |
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Notes: |
application/gzip |
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Label: |
clean_learning_concerns_data.rds |
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Text: |
learning data |
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Notes: |
application/gzip |
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Label: |
clean_vaxintake_data.csv |
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Text: |
intake survey data |
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Notes: |
text/csv |
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Label: |
codebook_concerns.pdf |
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Text: |
codebook for learning data |
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Notes: |
application/pdf |
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Label: |
codebook_eval.pdf |
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Text: |
codebook for evaluation data |
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Notes: |
application/pdf |
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codebook_vaxintakes.pdf |
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Text: |
codebook for intake survey |
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Notes: |
application/pdf |
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Label: |
README.md |
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Text: |
read me file |
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Notes: |
text/markdown |
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Label: |
requirements.txt |
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Text: |
packages required |
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Notes: |
text/plain |
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Label: |
utils.R |
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Text: |
supporting functions |
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Notes: |
type/x-r-syntax |