Using Social Listening and Digital Analytics to Address Diphtheria Misinformation During an Outbreak Response in Nigeria
1
Nigeria Health Watch, Utako, Abuja, Nigeria
Popul. Med. 2026;8(Supplement Supplement 1):A851
ABSTRACT
INTRODUCTION:
The rapid expansion of digital and social media platforms has transformed how communities' access and share health information, while simultaneously amplifying misinformation during disease outbreaks. During Nigeria’s recent diphtheria outbreak, misinformation threatened vaccine confidence and public trust. This study examines how social listening and digital analytics supported infodemic management and informed outbreak response, drawing on insights from the Health Information Disorder and Infodemic Management Network (HIDIM).
METHODS:
Systematic social media monitoring tracked diphtheria-related online discourse in Nigeria from October 7 to 6 November 2025 across X (formerly Twitter), Facebook, Instagram, YouTube, and web-based platforms. Using keyword-based social listening with an AI-powered digital analytics tool, data were collected and analyzed in Microsoft Excel to assess content volume, reach, engagement, sentiment, and misinformation themes. Duplicate and irrelevant content were excluded to ensure analytical accuracy.
RESULTS:
A total of 305 diphtheria-related online conversations reached 303887 people and generated 3904 engagements. ‘X’ accounted for the majority with 275 posts (244327 reach and 3642 engagements), followed by web platforms with 29 posts (59289 reach, and 261 engagements). YouTube recorded one post with 271 reach and one engagement, while Facebook and Instagram recorded no relevant activity during that period. A widely shared misinformation narrative alleged that Lagos Primary Health Centres administered Tetanus Toxoid while labeling it as diphtheria vaccine, fueling public mistrust and online debate about vaccine safety and accuracy.
CONCLUSIONS:
Social listening and digital analytics provide timely, actionable intelligence for managing infodemics during disease outbreaks, particularly in resource-limited settings. By leveraging AI-enabled monitoring, public health authorities can rapidly identify misinformation trends, address emerging knowledge gaps, and deploy targeted risk communication strategies that build vaccine confidence. This approach demonstrates how digital health innovation can support equitable outbreak response, enhance public trust and improve disease prevention outcomes across diverse contexts.
The rapid expansion of digital and social media platforms has transformed how communities' access and share health information, while simultaneously amplifying misinformation during disease outbreaks. During Nigeria’s recent diphtheria outbreak, misinformation threatened vaccine confidence and public trust. This study examines how social listening and digital analytics supported infodemic management and informed outbreak response, drawing on insights from the Health Information Disorder and Infodemic Management Network (HIDIM).
METHODS:
Systematic social media monitoring tracked diphtheria-related online discourse in Nigeria from October 7 to 6 November 2025 across X (formerly Twitter), Facebook, Instagram, YouTube, and web-based platforms. Using keyword-based social listening with an AI-powered digital analytics tool, data were collected and analyzed in Microsoft Excel to assess content volume, reach, engagement, sentiment, and misinformation themes. Duplicate and irrelevant content were excluded to ensure analytical accuracy.
RESULTS:
A total of 305 diphtheria-related online conversations reached 303887 people and generated 3904 engagements. ‘X’ accounted for the majority with 275 posts (244327 reach and 3642 engagements), followed by web platforms with 29 posts (59289 reach, and 261 engagements). YouTube recorded one post with 271 reach and one engagement, while Facebook and Instagram recorded no relevant activity during that period. A widely shared misinformation narrative alleged that Lagos Primary Health Centres administered Tetanus Toxoid while labeling it as diphtheria vaccine, fueling public mistrust and online debate about vaccine safety and accuracy.
CONCLUSIONS:
Social listening and digital analytics provide timely, actionable intelligence for managing infodemics during disease outbreaks, particularly in resource-limited settings. By leveraging AI-enabled monitoring, public health authorities can rapidly identify misinformation trends, address emerging knowledge gaps, and deploy targeted risk communication strategies that build vaccine confidence. This approach demonstrates how digital health innovation can support equitable outbreak response, enhance public trust and improve disease prevention outcomes across diverse contexts.
| eISSN: | 2654-1459 |
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