Expert System for Detecting Fear of Missing Out (FOMO) Symptoms on Social Media Trends Using the Dempster-Shafer Method

Abstract

Fear of Missing Out (FOMO) is a psychological phenomenon that has become increasingly prevalent alongside the growth of social media usage and the rapid emergence of digital trends. The continuous
exposure to online activities, social interactions, and trending content may influence individuals to experience anxiety, excessive curiosity, and a persistent desire to stay updated with information on social media platforms. This study aims to develop an expert system for detecting the symptoms and level of Fear of Missing Out (FOMO) related to social media trends by implementing the Dempster-Shafer method. The proposed method is applied to handle uncertainty in the diagnostic process by calculating belief and plausibility values based on user-provided symptoms. The system analyzes several indicators of FOMO, including difficulty controlling social media usage, spending excessive time accessing social media, feeling anxious when disconnected from online activities, frequently checking updates, and worrying about missing important events or information. The inference mechanism combines symptom evidence to determine the most probable FOMO level experienced by users. The system development process includes knowledge acquisition from experts, representation of symptoms and hypotheses, implementation of the Dempster-Shafer calculation, and system evaluation through testing. The testing results demonstrate that the developed expert system can provide diagnostic results that are consistent with expert assessments, achieving the highest confidence value for the High FOMO Level hypothesis of 0.80 or 80%. This research indicates that the proposed system can be utilized as an early detection tool to increase awareness of the psychological impacts of excessive social media use and support users in understanding their FOMO conditions.