Sigma Xi Student Showcase Presentation
Abstract
Keywords: emoji, context, personality, behavior science, digital communication
Facial emojis are increasingly used in digital communication as non-verbal signals (e.g., gestures, facial expressions, eye contact) are mostly unavailable. Emoji provide a way to express nonverbal conversational cues in digital communication. Previous research on emoji use hints that people with different personality traits might interpret emojis differently (e.g., Jones, Wurm, Norville, & Mullins, 2020). We aim to investigate the potential influence of individuals’ personalities on their emoji use in different contexts through a mixed-methods approach. For the pilot experiment, we surveyed 126 participants (age range 15- to 25-years) from China to collect data on the affective valence of 40 facial emojis. Twelve facial emojis were selected and classified into three categories (positive, neutral, and negative) based on their perceived valence. Later in the experiment, 114 participants are asked to read 18 paragraphs of short digital conversations with two contexts: positive and non-positive. Their task is to imagine themselves as the designated character in each conversation and choose the emoji that they think is the best fit to respond. The results turn out that participants are significantly more likely to use positive emojis in a positive context and negative emojis in non-positive contexts, with participants, have more fluctuations in choosing emojis under non-positive contexts. On the personality side, only the extroversion aspect of personality has statistical significance: under positive contexts, less extravagant participants tend to use more positive emoji in positive context compared to more extravagant participants; however, there is no significant difference between less and more extravagant participants in non-pos contexts. Theoretically, this study may contribute to a deeper understanding of the link between personality and emoji usage in different contexts. Practically, the results can help reducing misunderstandings in digital communication and improving user experiences in different social communication applications.