Understanding Social Media Experiences: A Cross-Cultural Exploration of FoMO, Personality Traits, and Social Media Communication Styles

Abstract

Social media has become an integral part of most people's daily lives. As the usage of these platforms grow, understanding how an individual’s social media experiences influences their mental health and well-being becomes more important. This thesis investigates the psychological and cultural factors that contribute to shaping an individual's social media engagement, by focusing on three key elements: Fear of Missing Out (FoMO), personality traits and social media communication styles. The data was gathered using a questionnaire and a total of 599 participants, 327 Arabs and 272 British, were included in the analysis of this thesis. Firstly, FoMO refers to the anxiety people experience about missing opportunities to connect, be part and participate in, whether online or in real-life. Using Linear Mixed Model, this thesis examines whether people misperceive FoMO in others compared to themselves by assessing their own experiences of FoMO and those of others. This thesis also investigates if social media fuels FoMO, by evaluating the experiences of FoMO on social media and real-life contexts, using Linear Mixed Model. Secondly, research suggests that personality is associated with social media engagement patterns. Therefore, this thesis explores which personality traits predict both social well-being on social media as well as social media disorder using Multivariate Multiple Linear Regression. Confirmatory Factor Analysis is used to assess if social well-being on social media can be represented as a single variable, to simply the analysis. Understanding which personality traits is associated with both positive and negative behavior can allow targeted interventions and coping strategies to enhance well-being on social media whilst reducing its negative effects. Thirdly, personality traits in combination with cultural differences can also influence social media communication styles. This thesis aims to explore how communication styles vary across distinct cultures and how personality traits affects this pattern. K-Means clustering is used to partition the dataset into distinct groups based on communication styles. General Linear Model is used to analyze personality differences across the clusters. The findings of this thesis reveal a complex interaction of psychological, behavior and cultural patterns that can affect social media behavior and online experiences, influencing overall well-being experienced online. This thesis contributes by offering a nuanced understanding of social media experiences that can be used to support healthier social media use and designing effective intervention strategies.

Date of Award	2025
Original language	American English
Awarding Institution	HBKU College of Science and Engineering