A Literature Review of Instagram User Interaction Patterns for Detecting Suspicious Bot Activities

Abstract

Instagram is one of the most widely used social media platforms and has become an important medium for communication, self-expression, and social interaction. However, the increasing use of Instagram is also accompanied by the emergence of suspicious activities carried out by bot accounts, which can negatively affect the credibility and security of the platform. This study aims to analyze Instagram user interaction patterns to detect suspicious activities performed by bot accounts. The research employed a qualitative descriptive approach using a literature review method with a narrative systematic review design. Data sources consisted of scientific journal articles indexed in SINTA and international databases published between 2024 and 2026, supported by books, proceedings, theses, and other relevant documents. The literature selection process was conducted through four stages: identification, screening, eligibility, and final inclusion, resulting in 15 primary references. The analysis focused on several interaction variables, including likes, comments, follows, unfollows, stories, and posts. The findings indicate significant differences between genuine users and bot accounts. Bot accounts exhibit hyperactive behavior characterized by unusually high interaction volumes and highly consistent, rigid activity patterns with narrow value ranges. In contrast, genuine users display more dynamic and fluctuating interaction patterns influenced by social and psychological factors. Therefore, suspicious bot activities on Instagram can be effectively identified through the analysis of interaction volume and statistical behavior patterns.