Characteristics Analysis of Posts Leading to Online Fraud and Crime on Social Media

Abstract

The recruitment of perpetrators for special frauds and robberies under the pretense of “high-paying part-time jobs” and the methods used to deceive victims into relinquishing money or valuables have emerged as critical issues on social media. Current approaches have been proposed to detect illegal and harmful content such as analyzing contextual features like slang in posts and identifying suspicious accounts through network graph analysis. However, their efficacy in addressing this threat remains unverified. This study seeks to elucidate the characteristics of these threats and evaluate the effectiveness and limitations of conventional detection methods. To achieve this, we collected and analyzed posts from X (formerly Twitter) that appeared to be associated with attempts to incite online fraud or criminal activities. The analysis revealed that posts likely associated with criminal activities often included specific place names and job details, suggesting that conventional detection methods may be partially effective in identifying such content. Conversely, posts potentially related to online fraud frequently employed language referencing the personal traits of individuals targeted for recruitment into high-paying part-time jobs. Additionally, these posts commonly utilized mentions and emojis, distinguishing them from those associated with criminal activities. This study's primary contribution is in demonstrating the scope and challenges of the previously studied methods and provides knowledge that can be applied to the automatic detection of posts on social media recruiting people for high remuneration.