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This study implemented a repeated-measures design with the voluntary respondents of different online attitude scales. Participants were recruited through a gaming application embedded in a popular online social network. Two online administrations were conducted to diagnose cyberfaking among 806 users. The data were merged into a single file to detect inconsistent responses which was conducted through matching individual responses with the nicknames. After the fakers and non-fakers were classified, they were compared with regard to their responses in attitude scales.