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Effect of Deepfake Health Information Awareness on Ability to Identify False Health Information among University Students in Nigeria

Abstract

The increasing use of artificial intelligence and digital media has created new challenges for the identification of false and misleading health information. Deepfake technology can generate or manipulate realistic audio, video, and visual content that may falsely portray healthcare professionals, public figures, institutions, or health-related events. University students in Nigeria increasingly obtain health information through social media, messaging applications, video platforms, and other digital sources, making them potentially vulnerable to sophisticated forms of health misinformation. Inability to distinguish authentic health information from AI-generated or manipulated content may contribute to inappropriate health decisions, delayed healthcare-seeking, acceptance of harmful health practices, and dissemination of false information to others. Deepfake health information awareness education provides an opportunity to improve students' understanding of deepfake technology, common characteristics of manipulated health content, verification methods, source evaluation, and responsible sharing of health information. Against this background, this study investigates the effect of deepfake health information awareness on the ability to identify false health information among university students in Nigeria. The study will be anchored on the Health Literacy Framework, Theory of Planned Behavior, and Social Cognitive Theory. The Health Literacy Framework emphasizes the ability to access, understand, evaluate, and appropriately apply health information obtained from digital and other sources. The Theory of Planned Behavior explains how attitudes, perceived social expectations, and perceived behavioural control may influence students' intentions to verify and critically evaluate health information before accepting or sharing it. Social Cognitive Theory explains how students may acquire information-verification skills through observation, modelling, practice, feedback, and reinforcement. Collectively, these theoretical perspectives provide a suitable framework for explaining how awareness of deepfake health information may influence students' ability to identify false health information. The study will adopt a quantitative quasi-experimental or analytical cross-sectional research design. The study population will comprise undergraduate and postgraduate students aged 18 years and above enrolled in selected public and private universities across Nigeria. A multistage sampling technique will be used to select geopolitical zones, states, universities, faculties or departments, and eligible students. Deepfake health information awareness will be assessed using indicators such as knowledge of deepfake technology, awareness of AI-generated health content, recognition of manipulated audio and video, understanding of common deepfake characteristics, awareness of source verification techniques, knowledge of reverse-image or video verification where applicable, awareness of trusted health-information sources, understanding of content manipulation, and knowledge of responsible information-sharing practices. Ability to identify false health information will be assessed using participants' performance when evaluating authentic, misleading, manipulated, and deepfake health-related content. Indicators may include identification of false claims, recognition of manipulated media, assessment of source credibility, verification of health claims, identification of suspicious features, comparison with authoritative health sources, appropriate classification of content, and decisions regarding whether information should be shared. Data will be collected using structured questionnaires, standardized digital health literacy and information-verification assessment tools, scenario-based or image/video identification tasks, and relevant educational intervention records. Descriptive statistics will be used to summarize participants' characteristics, digital-media use, deepfake awareness, and health-information verification performance. Inferential statistical techniques, including chi-square tests, t-tests, correlation analysis, and logistic or multiple regression analysis where appropriate, will be used to determine the effect of deepfake health information awareness on the ability to identify false health information. Where a quasi-experimental design is adopted, participants' identification scores before and after the awareness intervention may be compared with those of a comparison group to determine changes associated with the intervention. Diagnostic tests will also be conducted to assess the reliability, validity, and robustness of the findings. The study is expected to find that deepfake health information awareness has a significant positive effect on university students' ability to identify false health information in Nigeria. Students exposed to structured deepfake-awareness education are expected to perform better in identifying manipulated health-related content and distinguishing credible information from false or misleading information than students without comparable exposure. Awareness education may improve students' ability to question suspicious claims, evaluate the credibility of information sources, recognize inconsistencies in manipulated media, verify information through authoritative sources, and avoid sharing unverified health content. Practical demonstrations using simulated examples may strengthen students' ability to apply verification techniques rather than relying solely on visual appearance or familiarity with the person presenting the information. However, rapidly evolving AI technologies, sophisticated manipulation techniques, high levels of trust in familiar sources, limited digital health literacy, confirmation bias, and widespread exposure to misinformation may reduce the effectiveness of awareness interventions. The study therefore expects evidence-based, practical, and regularly updated deepfake health information awareness programmes to contribute significantly to improved identification of false health information among university students in Nigeria. The study is expected to contribute to the literature on deepfake technology, health misinformation, digital health literacy, health-information verification, artificial intelligence, media literacy, university student health, health communication, and public health in Nigeria. The findings will provide useful information to the Federal Ministry of Health and Social Welfare, Federal Ministry of Education, National Universities Commission, universities, university health centres, public health practitioners, healthcare professionals, digital health specialists, media organizations, technology educators, development partners, and policymakers regarding strategies for strengthening students' ability to evaluate digital health information. The study will also provide evidence-based recommendations for integrating AI and deepfake awareness into digital health literacy programmes, teaching practical health-information verification skills, promoting the use of authoritative health-information sources, strengthening responsible digital communication, and developing educational strategies that help young adults recognize and avoid false health information in Nigeria.

Keywords: Deepfake health information, health misinformation, false health information, artificial intelligence, digital health literacy, information verification, university students, media literacy, health communication, Nigeria, public health.