AI-Powered Social Media Analytics for Public Health Communication: A Case Study on Africa CDC

This project demonstrates the application of data science, natural language processing, and statistical analysis in evaluating the effectiveness of Africa CDC’s COVID-19 communication strategies on Facebook and Instagram during the pandemic (2020–2022). The study employed automated data scraping, structured content analysis, and statistical modeling to assess audience engagement and message framing.

The workflow began with automated data extraction using instaloader, BeautifulSoup4, selenium, and Parsehub to collect over 400 posts tagged with #COVID19 across both platforms. Each post’s metadata — including likes, shares, comments, content type, and influencer presence — was captured into structured datasets and cleaned using pandas in Python.

Subsequently, quantitative content analysis was performed using R, focusing on:

Crisis message types (protective, scientific, supportive)

Visual framing (photo, video, text-only)

Engagement metrics (average likes, comments, and shares)

Platform comparison (Facebook vs Instagram)

Key findings revealed that:

Africa CDC heavily favored text-only protective messages, which achieved higher engagement on Facebook than Instagram.

Posts featuring visuals or influencers were virtually absent, yet hypothetical modeling indicated these would significantly improve reach and engagement (up to 3× higher).

Peak engagement correlated with pandemic milestones, emphasizing the importance of timing and emotional resonance in crisis communication.

The study integrated theoretical frameworks such as the Crisis and Emergency Risk Communication (CERC) Model and Framing Theory to interpret patterns and propose evidence-based recommendations for public health institutions.

Tools & Technologies Used:
Python: instaloader, selenium, bs4, pandas for scraping and preprocessing

R: tidyverse, statistical analysis, data visualization (bar, pie, and line charts)

Parsehub: for scraping protected Facebook content

CSV/Excel: for data integration and storage

Impact & Insights:
Demonstrated how AI and data science can enhance digital health communications

Provided actionable insights into improving audience engagement through visual content, influencer marketing, and platform-specific strategies

Created a scalable framework for analyzing social media crisis communication using open-source tools

Skills Highlighted:
Social media data scraping & automation

Text and content analysis in health communication

R/Python interoperability for research

Statistical inference and engagement forecasting

Public health analytics and communication strategy evaluation