An Analytical Examination of Misinformation Spread Patterns on WhatsApp in Nigerian Conflict-Prone Contexts

WhatsApp's closed-group architecture makes it a particularly potent vector for misinformation in contexts where ethnic, religious, and political tensions create strong motivated reasoning for sharing unverified content, yet the specific spread dynamics of misinformation on WhatsApp in Nigerian conflict contexts remain analytically underexplored. This study analytically examined misinformation spread patterns on WhatsApp in Kaduna State and Benue State, two Nigerian contexts with documented histories of conflict-aggravating misinformation. A multi-method analytical design was employed, combining computational analysis of 840 archived WhatsApp messages from public community groups in both states (collected with informed consent through research partnerships with community leaders), expert interviews with 14 digital verification journalists, and a structured survey of 200 WhatsApp users assessing sharing motivations. Computational analysis coded messages for false content indicators, emotional valence, presence of ethnic or religious targeting, and spreading characteristics. Results showed that emotionally high-valence content was 3.4 times more likely to be widely forwarded than factual correction content. Ethnic identity cuing significantly increased forward probability within in-group networks. Conflict misinformation dominated spread within a 72-hour window before spike then declined, suggesting event-reactive rather than sustained spread dynamics. Verification labelling by WhatsApp reduced sharing probability by only 18 percent in the tested sample. The study provides original analytical evidence on misinformation dynamics in Nigerian conflict contexts, recommending community-trust intermediary verification models as more effective alternatives to platform-based labels.

Keywords: misinformation, WhatsApp, conflict communication, Nigeria, information spreading