This study analysed whether certain personal background attributes, such as age, gender, highest qualification and so on, impact on the ability to recognise whether the online news is fake or real. The study contributes to emerging research being carried out on fake news. The results of this study may help prevent the publication of fake news in the future.

This research applied a mixed methods approach to collecting and analysing the data. 89 participants were asked to answer a Fake News Test, a background questionnaire about ten background attributes and an interview with open-ended questions. The Fake News Test and background questionnaire data were analysed quantitatively using statistical analysis through SPSS statistical software. One-way ANOVA analysis was established to examine if any statistically significant relationships existed between the participants’ background attributes and their ability to recognise whether the news in the Fake News Test was fake or real. The transcribed interview data was analysed by using NVivo qualitative software through a process of coding, labelling and categorisation.

This quantitative component of the study found that, age, highest qualification and time spent on social media were the background attributes that significantly affected participants’ ability to recognise whether the news was fake or not. The qualitative analysis supported the quantitative results and showed that those participants who were better able to identify fake news from real news more actively read the news, had informed views and a deeper understanding of issues around fake news and usually checked the validity of the news that they read.