The COVID-19 “infodemic” has resulted in the widespread dissemination of counterfeit medical advice, hoaxes, fake products, and phoney information about the virus and responses. As a result, computational methods for determining any information’s authenticity to improve trust in public health awareness and policy decisions are profoundly discussed in the scientific community. Even before the pandemic, mis- and disinformation, including fake news, have been observed in the online world in significant numbers for numerous business, political and personal reasons. Moreover, many of these fake news was published from sources believed to be reliable. In contrast, some other fake news was fabricated in a way that would be easily trusted and shared by the general people in social media. COVID-19 related fake news has enormous effects on both the offline and online community, and thus, it challenges government initiatives for proper health intervention. Therefore, interest in research in this area has risen to understand the problem both socially and technically. In this paper, we attempt to understand how we can help student Internet users of colleges from the lower-middle-income country, Bangladesh, in Southeast Asia, to distinguish COVID-19 misinformation. Our study reveals that providing related news as supplementary information to any online news helps students make better decision about news authenticity. Statistical analyses on the survey data show that male students were found to be more accurate than female students to detect mis- and disinformation; students from the urban areas could detect misleading news better than students from villages; and that students from Science background demonstrated overall best performance, while students from Madrasah background, who are all male, could not produce a significant improvement. We conclude that the female students in general and male students of Madrasah, who spend the least amount of time online among all the student Internet users, are the most vulnerable groups to fake news.